Methods for assessment of multiple sclerosis activity

ABSTRACT

Markers useful for determining multiple sclerosis activity in a human subject are provided, along with kits for measuring quantitative expression values of the markers. Also provided are computer systems and software embodiments of predictive models for scoring and determining multiple sclerosis activity in human subjects based on the quantitative expression values of the markers.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/323,541, filed Apr. 15, 2016, and U.S. Provisional Application No.62/462,302, filed Feb. 22, 2017, the entire disclosures of which arehereby incorporated by reference in their entirety for all purposes.

BACKGROUND Field of the Invention

The invention relates to predictive models for assessment of multiplesclerosis activity in an individual based on biomarker expressionmeasurements from a sample obtained from the individual. The inventionfurther relates to the methods of use of the predictive models, and tocomputer systems, software, and kits for the implementation of suchpredictive models.

Description of the Related Art

Multiple Sclerosis (MS) is a chronic debilitating disease with highlyvariable outcomes. Currently there are few tools outside of magneticresonance imaging (MRI) to directly assess disease activity. Whileuseful, MRI reflects historic damage but not the dynamic biologicalprocesses that underlie MS.

The field is in need of tools to accurately determine multiple sclerosisactivity in an individual including diagnosing MS, tracking diseaseactivity, identifying early evidence of relapse, and determiningeffectiveness of treatment response. A non-invasive blood test thatcould reliably determine MS activity would have significant clinicalutility. Herein the development and validation of predictive models thatcan predict MS activity in an individual using quantitative expressionlevels of markers in a blood sample obtained from the individual isdescribed.

SUMMARY

In some embodiments, the invention relates to diagnosing MS in anindividual according to a prediction generated based on expressionvalues of one or more markers in a test sample obtained from theindividual. In other embodiments, the invention relates to assessingtherapeutic response in an individual diagnosed with multiple sclerosisaccording to a prediction generated based on expression values of one ormore markers in a test sample obtained from the individual. In yet otherembodiments, the invention relates to assessing disease activity in anindividual diagnosed with multiple sclerosis according to a predictiongenerated based on expression values of one or more markers in a testsample obtained from the individual. In other embodiments, the inventionrelates to assessing relapse and flare in an individual diagnosed withmultiple sclerosis according to a prediction generated based onexpression values of one or more markers in a test sample obtained fromthe individual. In other embodiments, the invention relates to assessingremission in an individual diagnosed with multiple sclerosis accordingto a prediction generated based on expression values of one or moremarkers in a test sample obtained from the individual.

In various embodiments of the invention, the prediction model is trainedusing one of random forest (RF), stochastic gradient boosting (GBM),Lasso, or extreme gradient boosting (XGB) machine learning techniques.The prediction model can be applied to a test sample obtained from theindividual in order to generate an assessment that can be used to guidethe diagnosis of multiple sclerosis in an individual, assessment of atherapeutic response in an individual diagnosed with multiple sclerosis,assessment of disease activity in an individual diagnosed with multiplesclerosis, assessment of relapse and flare in an individual diagnosedwith multiple sclerosis, or assessment of remission in an individualdiagnosed with multiple sclerosis.

Disclosed herein is a method for assessing multiple sclerosis activityin an individual, the method comprising: obtaining a dataset comprisingquantitative expression values for a plurality of biomarkers from a testsample from the individual, wherein the plurality of biomarkers comprisetwo or more biomarkers as shown in one or more of set 1, set 2, set 3,set 4, and set 5, wherein set 1 comprises PON1, Myoglobin, PAI1, TIMP1,SDF1, IL6Rbeta, Cystatin B, IgE, MIP3beta, and VCAM1, wherein set 2comprises MDC, VEGF, Ficolin 3, IgA, Factor VII, IL6R, RAGE, FIB1C,ITAC, and GH, wherein set 3 comprises HBEGF, NrCAM, GROalpha, GDF15,SCFR, Ecad, Angiogenin, Sortilin, AAT, IgM, PARC, SP-D, BAFF, ADM, PEDF,IL1ra, TBG, Microalbumin, Leptin, and Eotaxin 2, wherein set 4 comprisesIGFBP2, Resistin, Cathepsin D, E-Selectin, YKL40, IL22, IL8, CA 15-3,LeptinR, IGFBP2, MCP1, PRL, Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1,HCC-4, and C3, and wherein set 5 comprises AFP, ANG-1, IL18, Gelsolin,TN-C, Vitronectin, B2M, TATI, MMP3, Omentin, IL 18bp, ApoD, MCP-4,Apo-E, ST2, Thrombospondin 1, GIP, MMP7, ICAM-1, and DKK1; applying apredictive model on the obtained dataset to generate a score; anddetermining multiple sclerosis activity in the individual based on thescore.

In some embodiments, the dataset comprises quantitative expressionvalues for PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B,IgE, MIP3beta, and VCAM1, wherein the multiple sclerosis activity in theindividual is a state of quiescence or exacerbation, wherein aperformance of the predictive model is characterized by an area underthe curve (AUC) ranging from 0.60 to 0.99, and wherein determiningmultiple sclerosis activity in the individual based on the scorecomprises comparing the generated score to a distribution of scores, thedistribution of scores corresponding to individuals previously diagnosedwith multiple sclerosis that have been clinically classified as being inone of a state of quiescence or exacerbation; and classifying theindividual as being in one of the state of quiescence or exacerbationbased on the comparison.

In some scenarios, the dataset comprises quantitative expression valuesfor ten or more biomarkers. In some scenarios, at least five of the tenor more biomarkers are selected from biomarkers in set 1. In somescenarios, the dataset comprises quantitative expression values fortwenty or more biomarkers. In some scenarios, at least ten of the twentyor more biomarkers are selected from biomarkers in set 1 and set 2. Insome scenarios, the dataset comprises quantitative expression values forforty or more biomarkers. In some scenarios, at least twenty of theforty or more biomarkers are selected from biomarkers in set 1, set 2,and set 3. In some scenarios, the dataset comprises quantitativeexpression values for sixty or more biomarkers. In some scenarios, atleast thirty of the sixty or more biomarkers are selected frombiomarkers in set 1, set 2, set 3, and set 4.

In some embodiments, the predictive model is trained using one of arandom forest algorithm, a gradient boosting algorithm, and a Lassoalgorithm. In one scenario, performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.60 to0.99. In one scenario, performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.70 to0.99. In one scenario, performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.80 to0.99.

In some embodiments, the step of obtaining the dataset comprisescarrying out a multiplex immunoassay on the test sample from theindividual. In some embodiments, obtaining the dataset from the testsample comprises obtaining the test sample and processing the testsample to experimentally determine the dataset. In some embodiments,obtaining the dataset from the test sample comprises receiving thedataset from a third party that has processed the test sample toexperimentally determine the dataset.

In various embodiments, the quantitative expression values for theplurality of biomarkers are adjusted based on at least one of age andgender of the individual. In one scenario, the individual is a human. Inone scenario, the test sample from the individual is a blood sample.

In some scenarios, determining multiple sclerosis activity comprisesdetermining a state of multiple sclerosis in the individual, wherein thestate is quiescence or exacerbation. In some scenarios, determiningmultiple sclerosis activity comprises diagnosing the individual withmultiple sclerosis.

In various embodiments, determining multiple sclerosis activity in theindividual based on the score comprises: comparing the generated scoreto a distribution of scores, the distribution of scores corresponding toindividuals that have been previously classified in one of a pluralityof categories of multiple sclerosis activity. In some embodiments, theprevious classification of individuals in the category of multiplesclerosis activity is based on clinical standards.

Also disclosed herein is a method for generating a predictive model forpredicting multiple sclerosis activity, the method comprising: obtainingtraining data derived from a plurality of individuals, the training datacomprising: for each individual from the plurality of individuals:quantitative expression values of a plurality of biomarkers derived froma test sample obtained from the individual, wherein the plurality ofbiomarkers comprise two or more biomarkers selected from a groupconsisting of biomarkers from set 1, set 2, set 3, set 4, and set 5,wherein set 1 comprises PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta,Cystatin B, IgE, MIP3beta, and VCAM1, wherein set 2 comprises MDC, VEGF,Ficolin 3, IgA, Factor VII, IL6R, RAGE, FIB1C, ITAC, and GH, wherein set3 comprises HBEGF, NrCAM, GROalpha, GDF15, SCFR, Ecad, Angiogenin,Sortilin, AAT, IgM, PARC, SP-D, BAFF, ADM, PEDF, IL1ra, TBG,Microalbumin, Leptin, and Eotaxin 2, wherein set 4 comprises IGFBP2,Resistin, Cathepsin D, E-Selectin, YKL40, IL22, IL8, CA 15-3, LeptinR,IGFBP2, MCP1, PRL, Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1, HCC-4, andC3, and wherein set 5 comprises AFP, ANG-1, IL18, Gelsolin, TN-C,Vitronectin, B2M, TATI, MMP3, Omentin, IL 18bp, ApoD, MCP-4, Apo-E, ST2,Thrombospondin 1, GIP, MMP7, ICAM-1, and DKK1; and an indication as tothe multiple sclerosis activity of the individual; training thepredictive model using the obtained training data, wherein thepredictive model is trained on inputs comprising the quantitativeexpression values of the plurality of biomarkers and on ground truthdata comprising the indication.

In one scenario, the plurality of biomarkers comprise ten or morebiomarkers. In one scenario, at least five of the ten or more biomarkersare selected from biomarkers in set 1. In one scenario, the plurality ofbiomarkers comprise twenty or more biomarkers. In one scenario, at leastten of the twenty or more biomarkers are selected from biomarkers in set1 and set 2. In one scenario, the plurality of biomarkers comprise fortyor more biomarkers. In one scenario, at least twenty of the forty ormore biomarkers are selected from biomarkers in set 1, set 2, and set 3.In one scenario, the plurality of biomarkers comprise sixty or morebiomarkers. In one scenario, at least thirty of the sixty or morebiomarkers are selected from biomarkers in set 1, set 2, set 3, and set4.

In some embodiments, biomarkers in set 1, set 2, set 3, set 4, and set 5are ranked based on an importance of each biomarker for determiningmultiple sclerosis activity, wherein biomarkers in set 1 are rankedhigher than biomarkers in set 2, wherein biomarkers in set 2 are rankedhigher than biomarkers in set 3, wherein biomarkers in set 3 are rankedhigher than biomarkers in set 4, and wherein biomarkers in set 4 areranked higher than biomarkers in set 5.

In some embodiments, training the prediction model comprises trainingthe prediction model using one of a random forest algorithm, gradientboosting algorithm, and Lasso algorithm.

In various embodiments, each individual of the plurality of individualsis a human. In some embodiments, the test sample obtained from theindividual is a blood sample. In some embodiments, the predictive modeldetermines a state of multiple sclerosis in an individual, wherein thestate is quiescence or exacerbation. In some embodiments, the predictivemodel determines whether to diagnose the individual with multiplesclerosis.

Also disclosed herein is a system for determining multiple sclerosisactivity in an individual, the system comprising: a storage memory forstoring a dataset comprising quantitative expression values for aplurality of biomarkers from a test sample from the individual, whereinthe plurality of biomarkers comprise two or more biomarkers as shown inone or more of set 1, set 2, set 3, set 4, and set 5, wherein set 1comprises PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B, IgE,MIP3beta, and VCAM1, wherein set 2 comprises MDC, VEGF, Ficolin 3, IgA,Factor VII, IL6R, RAGE, FIB1C, ITAC, and GH, wherein set 3 comprisesHBEGF, NrCAM, GROalpha, GDF15, SCFR, Ecad, Angiogenin, Sortilin, AAT,IgM, PARC, SP-D, BAFF, ADM, PEDF, IL1ra, TBG, Microalbumin, Leptin, andEotaxin 2, wherein set 4 comprises IGFBP2, Resistin, Cathepsin D,E-Selectin, YKL40, IL22, IL8, CA 15-3, LeptinR, IGFBP2, MCP1, PRL,Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1, HCC-4, and C3, and wherein set5 comprises AFP, ANG-1, IL18, Gelsolin, TN-C, Vitronectin, B2M, TATI,MMP3, Omentin, IL 18bp, ApoD, MCP-4, Apo-E, ST2, Thrombospondin 1, GIP,MMP7, ICAM-1, and DKK1; and a processor communicatively coupled to thestorage memory for determining a score by applying the stored dataset asinput to a predictive model, wherein the score is predictive of anassessment of multiple sclerosis activity in the individual.

In some embodiments, the dataset comprises quantitative expressionvalues for PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B,IgE, MIP3beta, and VCAM1, wherein the multiple sclerosis activity in theindividual is a state of quiescence or exacerbation, wherein aperformance of the predictive model is characterized by an area underthe curve (AUC) ranging from 0.60 to 0.99, and wherein the assessment ofmultiple sclerosis activity in the individual is determined by comparingthe determined score to a distribution of scores, the distribution ofscores corresponding to individuals previously diagnosed with multiplesclerosis that have been clinically classified as being in one of astate of quiescence or exacerbation.

In one scenario, the dataset comprises quantitative expression valuesfor ten or more biomarkers. In one scenario, at least five of the ten ormore biomarkers are selected from biomarkers in set 1. In one scenario,the dataset comprises quantitative expression values for twenty or morebiomarkers. In one scenario, at least ten of the twenty or morebiomarkers are selected from biomarkers in set 1 and set 2. In onescenario, the dataset comprises quantitative expression values for fortyor more biomarkers. In one scenario, at least twenty of the forty ormore biomarkers are selected from biomarkers in set 1, set 2, and set 3.In one scenario, the dataset comprises quantitative expression valuesfor sixty or more biomarkers. In one scenario, at least thirty of thesixty or more biomarkers are selected from biomarkers in set 1, set 2,set 3, and set 4.

In various embodiments, the predictive model is trained using one of arandom forest algorithm, a gradient boosting algorithm, and a Lassoalgorithm. In some embodiments, the performance of the predictive modelis characterized by an area under the curve (AUC) ranging from 0.60 to0.99. In some embodiments, the performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.70 to0.99. In some embodiments, the performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.80 to0.99.

In various embodiments, the dataset is obtained from a multipleximmunoassay performed on the test sample from the individual. In someembodiments, the dataset is experimentally determined by processing thetest sample. In some embodiments, the dataset is received from a thirdparty that has processed the test sample to experimentally determine thedataset.

In various embodiments, the quantitative expression values for theplurality of biomarkers are adjusted based on at least one of age andgender of the individual. In one scenario, the individual is a human. Inone scenario, the test sample from the individual is a blood sample. Inone embodiment, the assessment of multiple sclerosis activity indicatesa state of multiple sclerosis in the individual, wherein the state isquiescence or exacerbation. In one embodiment, the assessment ofmultiple sclerosis activity indicates a diagnosis of multiple sclerosis.

In some embodiments, the assessment of multiple sclerosis activity inthe individual is determined by comparing the determined score to adistribution of scores, the distribution of scores corresponding toindividuals that have been previously classified in one of a pluralityof categories of multiple sclerosis activity. In some embodiments, theprevious classification of individuals in the category of multiplesclerosis activity is based on clinical standards.

Also disclosed herein is a non-transitory computer-readable mediumstoring computer code that, when executed by a processor of a computer,causes the processor to: obtain a dataset comprising quantitativeexpression values for a plurality of biomarkers from a test sample fromthe individual, wherein the plurality of biomarkers comprise two or morebiomarkers as shown in one or more of set 1, set 2, set 3, set 4, andset 5, wherein set 1 comprises PON1, Myoglobin, PAI1, TIMP1, SDF1,IL6Rbeta, Cystatin B, IgE, MIP3beta, and VCAM1, wherein set 2 comprisesMDC, VEGF, Ficolin 3, IgA, Factor VII, IL6R, RAGE, FIB1C, ITAC, and GH,wherein set 3 comprises HBEGF, NrCAM, GROalpha, GDF15, SCFR, Ecad,Angiogenin, Sortilin, AAT, IgM, PARC, SP-D, BAFF, ADM, PEDF, IL1ra, TBG,Microalbumin, Leptin, and Eotaxin 2, wherein set 4 comprises IGFBP2,Resistin, Cathepsin D, E-Selectin, YKL40, IL22, IL8, CA 15-3, LeptinR,IGFBP2, MCP1, PRL, Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1, HCC-4, andC3, and wherein set 5 comprises AFP, ANG-1, IL18, Gelsolin, TN-C,Vitronectin, B2M, TATI, MMP3, Omentin, IL 18bp, ApoD, MCP-4, Apo-E, ST2,Thrombospondin 1, GIP, MMP7, ICAM-1, and DKK1; and apply a predictivemodel on the obtained dataset to generate a score; and determinemultiple sclerosis activity in the individual based on the score.

In various embodiments, the dataset comprises quantitative expressionvalues for PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B,IgE, MIP3beta, and VCAM1, wherein the multiple sclerosis activity in theindividual is a state of quiescence or exacerbation, wherein aperformance of the predictive model is characterized by an area underthe curve (AUC) ranging from 0.60 to 0.99, and wherein the the computercode that causes the processor to determine multiple sclerosis activityin the individual based on the score further comprises computer codethat causes the processor to: compare the generated score to adistribution of scores, the distribution of scores corresponding toindividuals previously diagnosed with multiple sclerosis that have beenclinically classified as being in one of a state of quiescence orexacerbation; and classify the individual as being in one of the stateof quiescence or exacerbation based on the comparison.

In one scenario, the dataset comprises quantitative expression valuesfor ten or more biomarkers. In one scenario, at least five of the ten ormore biomarkers are selected from biomarkers in set 1.

In one scenario, the dataset comprises quantitative expression valuesfor twenty or more biomarkers. In one scenario, at least ten of thetwenty or more biomarkers are selected from biomarkers in set 1 and set2. In one scenario, the dataset comprises quantitative expression valuesfor forty or more biomarkers. In one scenario, at least twenty of theforty or more biomarkers are selected from biomarkers in set 1, set 2,and set 3. In one scenario, the dataset comprises quantitativeexpression values for sixty or more biomarkers. In one scenario, atleast thirty of the sixty or more biomarkers are selected frombiomarkers in set 1, set 2, set 3, and set 4.

In various embodiments, the predictive model is trained using one of arandom forest algorithm, a gradient boosting algorithm, and a Lassoalgorithm. In one scenario, performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.60 to0.99. In one scenario, performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.70 to0.99. In one scenario, performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.80 to0.99.

In various embodiments, the obtained dataset is experimentallydetermined by processing the test sample. In some embodiments, theobtained dataset is received from a third party that has processed thetest sample to experimentally determine the dataset. In someembodiments, the quantitative expression values for the plurality ofbiomarkers are adjusted based on at least one of age and gender of theindividual.

In one embodiment, the individual is a human. In some embodiments, thetest sample from the individual is a blood sample. In some embodiments,the assessment of multiple sclerosis activity in the individual is anindication of a state of multiple sclerosis in the individual, whereinthe state is quiescence or exacerbation. In some embodiments, theassessment of multiple sclerosis activity in the individual is adiagnosis of multiple sclerosis in the individual.

In various embodiments, the computer code that causes the processor todetermine multiple sclerosis activity in the individual based on thescore further comprises computer code that causes the processor tocompare the generated score to a distribution of scores, thedistribution of scores corresponding to individuals that have beenpreviously classified in one of a plurality of categories of multiplesclerosis activity. In some embodiments, the previous classification ofindividuals in the category of multiple sclerosis activity is based onclinical standards.

Also disclosed herein is a kit for diagnosing multiple sclerosis in anindividual, the kit comprising: a set of reagents for determining, froma test sample obtained from the individual, quantitative expressionvalues for a plurality of biomarkers from a test sample from theindividual, wherein the plurality of biomarkers comprise two or morebiomarkers as shown in one or more of set 1, set 2, set 3, set 4, andset 5, wherein set 1 comprises PON1, Myoglobin, PAI1, TIMP1, SDF1,IL6Rbeta, Cystatin B, IgE, MIP3beta, and VCAM1, wherein set 2 comprisesMDC, VEGF, Ficolin 3, IgA, Factor VII, IL6R, RAGE, FIB1C, ITAC, and GH,wherein set 3 comprises HBEGF, NrCAM, GROalpha, GDF15, SCFR, Ecad,Angiogenin, Sortilin, AAT, IgM, PARC, SP-D, BAFF, ADM, PEDF, IL1ra, TBG,Microalbumin, Leptin, and Eotaxin 2, wherein set 4 comprises IGFBP2,Resistin, Cathepsin D, E-Selectin, YKL40, IL22, IL8, CA 15-3, LeptinR,IGFBP2, MCP1, PRL, Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1, HCC-4, andC3, and wherein set 5 comprises AFP, ANG-1, IL18, Gelsolin, TN-C,Vitronectin, B2M, TATI, MMP3, Omentin, IL 18bp, ApoD, MCP-4, Apo-E, ST2,Thrombospondin 1, GIP, MMP7, ICAM-1, and DKK1; instructions for usingthe set of reagents to determine the quantitative expression values ofthe test sample, wherein the instructions further comprise instructionsfor determining a score from the quantitative expression values, whereinthe score is predictive of an assessment of multiple sclerosis activityin the individual.

In some embodiments, the set of reagents comprises reagents fordetermining quantitative expression values for PON1, Myoglobin, PAI1,TIMP1, SDF1, IL6Rbeta, Cystatin B, IgE, MIP3beta, and VCAM1, wherein themultiple sclerosis activity in the individual is a state of quiescenceor exacerbation, wherein a performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.60 to0.99, and wherein the assessment of multiple sclerosis activity in theindividual is determined by comparing the determined score to adistribution of scores, the distribution of scores corresponding toindividuals previously diagnosed with multiple sclerosis that have beenclinically classified as being in one of a state of quiescence orexacerbation.

In one scenario, the set of reagents comprise reagents for determiningquantitative expression values for ten or more biomarkers. In onescenario, at least five of the ten or more biomarkers are selected frombiomarkers in set 1. In one scenario, the set of reagents comprisereagents for determining quantitative expression values for twenty ormore biomarkers. In one scenario, at least ten of the twenty or morebiomarkers are selected from biomarkers in set 1 and set 2. In onescenario, the set of reagents comprise reagents for determiningquantitative expression values for forty or more biomarkers. In onescenario, at least twenty of the forty or more biomarkers are selectedfrom biomarkers in set 1, set 2, and set 3. In one scenario, the set ofreagents comprise reagents for determining quantitative expressionvalues for sixty or more biomarkers. In one scenario, at least thirty ofthe sixty or more biomarkers are selected from biomarkers in set 1, set2, set 3, and set 4.

In some embodiments, the instructions further comprise instructions forapplying a predictive model to generate the assessment of multiplesclerosis activity, the predictive model trained using one of a randomforest algorithm, a gradient boosting algorithm, and a Lasso algorithm.In some embodiments, the performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.60 to0.99. In some embodiments, the performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.70 to0.99. In some embodiments, the performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.80 to0.99.

In various embodiments, the set of reagents are for performing amultiplex immunoassay on the test sample from the individual and whereinthe quantitative expression values of the plurality of biomarkers areobtained from the performed multiplex immunoassay.

In some embodiments, the instructions further comprise instructions foradjusting the quantitative expression values for the plurality ofbiomarkers based on at least one of age and gender of the individual.

In one embodiment, the individual is a human. In one embodiment, thetest sample from the individual is a blood sample. In one embodiment,the assessment of multiple sclerosis activity is an indication of astate of multiple sclerosis in the individual, wherein the state isquiescence or exacerbation. In one embodiment, the assessment ofmultiple sclerosis activity is a diagnosis of multiple sclerosis in theindividual.

In some embodiments, the assessment of multiple sclerosis activity inthe individual is determined by comparing the determined score to adistribution of scores, the distribution of scores corresponding toindividuals that have been previously classified in one of a pluralityof categories of multiple sclerosis activity. In some embodiments, theprevious classification of individuals in the category of multiplesclerosis activity is based on clinical standards.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood with regard to the followingdescription, and accompanying drawings, where:

FIG. 1 depicts an overview of an environment for assessing MS activityin an individual via an activity prediction system, in accordance withan embodiment.

FIG. 2 depicts a block diagram illustrating the computer logiccomponents of the activity prediction system, in accordance with anembodiment.

FIG. 3 illustrates an example set of training data, in accordance withan embodiment.

FIG. 4 depicts identified markers that are upregulated in MS patients.

FIG. 5 depicts identified markers that are downregulated in MS patients.

FIG. 6 illustrates an example computer for implementing the entitiesshown in FIGS. 1 and 2.

DETAILED DESCRIPTION I. Definitions

In general, terms used in the claims and the specification are intendedto be construed as having the plain meaning understood by a person ofordinary skill in the art. Certain terms are defined below to provideadditional clarity. In case of conflict between the plain meaning andthe provided definitions, the provided definitions are to be used.

The term “multiple sclerosis” or “MS” encompasses all forms of multiplesclerosis including relapsing-remitting multiple sclerosis (RRMS),secondary progressive multiple sclerosis (SPMS), primary-progressivemultiple sclerosis (PPMS), and progressive relapsing multiple sclerosis(PRMS).

The terms “marker,” “markers,” “biomarker,” and “biomarkers” encompass,without limitation, lipids, lipoproteins, proteins, cytokines,chemokines, growth factors, peptides, nucleic acids, genes, andoligonucleotides, together with their related complexes, metabolites,mutations, variants, polymorphisms, modifications, fragments, subunits,degradation products, elements, and other analytes or sample-derivedmeasures. A marker can also include mutated proteins, mutated nucleicacids, variations in copy numbers, and/or transcript variants, incircumstances in which such mutations, variations in copy number and/ortranscript variants are useful for generating a predictive model, or areuseful in predictive models developed using related markers (e.g.,non-mutated versions of the proteins or nucleic acids, alternativetranscripts, etc.).

The term “multiple sclerosis activity” encompasses, without limitation,the presence or absence of of multiple sclerosis in an individual, astate (e.g., quiescent vs exacerbation) of multiple sclerosis in anindividual, a relapse or flare event associated with MS, a response ofan individual diagnosed with multiple sclerosis to a therapy, a degreeof multiple sclerosis disability, and a risk (e.g., likelihood) of theindividual developing multiple sclerosis at a subsequent time.

The term “antibody” is used in the broadest sense and specificallycovers monoclonal antibodies (including full length monoclonalantibodies), polyclonal antibodies, multispecific antibodies (e.g.,bispecific antibodies), and antibody fragments that are antigen-bindingso long as they exhibit the desired biological activity, e.g., anantibody or an antigen-binding fragment thereof.

“Antibody fragment”, and all grammatical variants thereof, as usedherein are defined as a portion of an intact antibody comprising theantigen binding site or variable region of the intact antibody, whereinthe portion is free of the constant heavy chain domains (i.e. CH2, CH3,and CH4, depending on antibody isotype) of the Fc region of the intactantibody. Examples of antibody fragments include Fab, Fab′, Fab′-SH,F(ab′)₂, and Fv fragments; diabodies; any antibody fragment that is apolypeptide having a primary structure consisting of one uninterruptedsequence of contiguous amino acid residues (referred to herein as a“single-chain antibody fragment” or “single chain polypeptide”).

The term “mammal” encompasses both humans and non-humans and includesbut is not limited to humans, non-human primates, canines, felines,murines, bovines, equines, and porcines.

The term “sample” can include a single cell or multiple cells orfragments of cells or an aliquot of body fluid, such as a blood sample,taken from a subject, by means including venipuncture, excretion,ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping,surgical incision, or intervention or other means known in the art.

The term “subject” encompasses a cell, tissue, or organism, human ornon-human, whether in vivo, ex vivo, or in vitro, male or female.

The term “obtaining a dataset associated with a sample” encompassesobtaining a set of data determined from at least one sample. Obtaining adataset encompasses obtaining a sample, and processing the sample toexperimentally determine the data. The phrase also encompasses receivinga set of data, e.g., from a third party that has processed the sample toexperimentally determine the dataset. Additionally, the phraseencompasses mining data from at least one database or at least onepublication or a combination of databases and publications. A datasetcan be obtained by one of skill in the art via a variety of known waysincluding stored on a storage memory.

It must be noted that, as used in the specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless the context clearly dictates otherwise.

II. Methods of Assessing Multiple Sclerosis Activity in an Individual

Disclosed herein are methods for assessing MS activity in an individual.As one example, one such method may include the steps of: obtaining adataset including quantitative expression values for one or more markersfrom a test sample obtained from the individual; applying a predictivemodel on the obtained dataset to generate an assessment of MS activityin the individual. Such a method can be computer-implemented using aprocessor or may be embodied in a kit that includes reagents forobtaining the dataset and/or instructions for generating an assessmentof MS activity.

Also disclosed herein are methods for generating a predictive model thatcan be used to assess MS activity in an individual. As one example, onesuch method may include the steps of obtaining training data derivedfrom multiple individuals, where the training data includes quantitativeexpression values of one or more markers from test samples obtained fromeach of the multiple individuals as well as indications as to the MSactivity in each of the multiple individuals; and training a predictivemodel using the obtained training data. Specifically, the predictivemodel can be trained on inputs that include the quantitative expressionvalues and ground truth data that includes the indications as to the MSactivity.

Overall, described herein is a robust, stepwise process for identifyinga panel or panels of biomarkers that are strongly predictive of MS.Univariate and multivariate analysis of specific biomarkers as describedherein demonstrate the ability to predict MS activity in an individual.Given the diverse pathology of the disease as well as diverse outcomesthat result from treatment of the disease, the methods of the presentteachings may be useful in the clinical assessment of MS in individualsubjects

Biomarkers used for the assessment of MS activity in an individual areidentified through an identification and ranking process. An exemplaryprocess includes obtaining test samples, such as blood samples, from apopulation of individuals (e.g., population in the Accelerated CureProject (ACP)), as described herein. The test samples are analyzed toobtain a dataset that includes expression values of biomarkers. Forexample, expression values of biomarkers can be obtained by applying thetest samples to a multi-plex immunoassay.

In some embodiments, test samples can be derived through a variety ofmethods, including prospective, retrospective, cross-sectional, orlongitudinal studies that involve interventions or observations of therepresentative subjects or populations from one or more time points.Test samples can be obtained from a single study or multiple studies.Subject and population data can generally include data pertaining to thesubjects' disease status and/or clinical assessments, which can be used,in addition to biomarker data obtained from test samples, for building,training, and validating a predictive model (e.g., algorithms) for usein the present teachings.

In some embodiments, predictive models are built using expression valuesof a single biomarker (e.g., univariate prediction). In someembodiments, predictive models are built using expression values of twoor more biomarkers (e.g., multivariate prediction). In some embodiments,predictive models are built based on expression values of biomarkersthat are identified to be most important for predicting MS activity inan individual. For example, various methods, such as random forest (RF),gradient boosting (GBM), extreme gradient boosting (XBM), and/or leastabsolute shrinkage and selection operator (LASSO) can be used todetermine the importance of each individual biomarker. Variouspredictive models can be built based on a variety of criteria such asthe biomarker rankings. For example, individual predictive models can bebuilt using expression values of a top 80, top 60, top 40, top 20, top10, or even top 2 biomarkers. Other criteria to be considered whenbuilding a model includes any improvement in a predictive model'sperformance when the biomarker is added to the predictive model.Predictive models can include various machine learning models such asdecision tree, an ensemble (e.g., bagging, boosting, random forest),linear regression, Naïve Bayes, neural network, or logistic regression.

Predictive models are trained using training data to better predict MSactivity in an individual. Specifically, a predictive model can betrained using training data that includes quantitative expression levelsof the selected biomarkers (e.g., 2, 10, 20, 40, 60, or 80 biomarkersthat are input to the predictive model) as well as ground truth datathat includes an indication as to the MS activity (e.g., quiescent orexacerbated state of MS) in the individual.

Trained predictive models can be evaluated and/or selected based onvarious performance and/or accuracy criteria, such as are describedherein. The predictive ability of a model can be evaluated according toits ability to provide a quality metric, e.g. area under the curve (AUC)or accuracy, of a particular value, or range of values. In someembodiments, a desired quality threshold is a predictive model that willclassify a sample with an accuracy of at least about 0.6, at least about0.65, at least about 0.7, at least about 0.75, at least about 0.8, atleast about 0.85, at least about 0.9, at least about 0.95, or higher. Asan alternative measure, a desired quality threshold can refer to apredictive model that will classify a sample with an AUC (area under thecurve) of at least about 0.6, at least about 0.65, at least about 0.7,at least about 0.75, at least about 0.8, at least about 0.85, at leastabout 0.9, or higher. Classification can be made according to predictivemodeling methods that set a threshold for determining the probabilitythat a sample belongs to a given class. The probability preferably is atleast 50%, or at least 60% or at least 70% or at least 80% or higher.

Trained predictive models can be stored and subsequently retrieved whenneeded. For example, during execution, a trained predictive model isselected and applied to expression values of biomarkers from a testsample obtained from an individual of interest. The predictive model canoutput an assessment of MS activity in the individual of interest.

III. Multiple Sclerosis Activity in an Individual

As used hereafter in this disclosure, MS activity refers can refer toany one of the presence of multiple sclerosis in an individual, a state(e.g., quiescent vs exacerbation) of multiple sclerosis in anindividual, a response of an individual diagnosed with multiplesclerosis to a therapy, a degree of multiple sclerosis disability, and arisk (e.g., likelihood) of the individual developing multiple sclerosisat a subsequent time.

Methods described herein focus on assessing MS activity in an individualby applying quantitative expression levels of biomarkers as input to atrained prediction model. In various embodiments, the assessment of MSactivity is used to further train the prediction model and/or isvalidated. Results corresponding to an individual of interest may becompared to results of individuals that have been previously classifiedin one of two or more categories. For example, individuals may bepreviously categorized such as a positive diagnosis of MS, acategorization in a quiescent or exacerbated state, a categorization ina level of disability according to the expanded disability status scale(EDSS), an identified clinical response to a therapy, and a clinicalidentification of a risk of developing MS. Categorization of previouslyindividuals may occur based on clinical standards.

Clinical diagnosis of MS can occur through various methods. As anexample, a clinical diagnosis of MS can be made through magneticresonance imaging (MRI) of the brain and spinal cord to identify lesionsor plaques that form as a result of MS. The McDonald criteria can beemployed in making the diagnosis. Clinical diagnosis of MS can alsooccur through a lumbar puncture (spinal tap) that observes abnormalitiesin antibody concentrations in the spinal fluid due to the presence ofMS. Clinical diagnosis of MS can also occur through evoked potentialtests, where electrical signals produced by neurons of the nervoussystem are recorded in response to a stimulus. An impaired transmissionis indicative of the presence of MS.

Clinical categorization of a patient previously diagnosed with MS in aquiescent state versus an exacerbated state can depend on a variety offactors. Namely, a patient can be clinically categorized in anexacerbated state after presenting with a new disease that is related toMS such as optic neuritis. As another example, a patient is clinicallycategorized in an exacerbated state if the patient presents withsignificant worsening of symptoms. Examples may include a worsening ofbalance and/or mobility, vision, pain in the eye, fatigue, and/orheart-related problems. Patients previously diagnosed with MS can beclinically categorized in a quiescent state if the patient does notpresent with a new disease or a change or worsening of symptoms.

Determination that a patient previously diagnosed with MS is respondingto a therapy can be dependent on a variety of clinical variables. Forexample, a response to therapy can be determined based on the occurrenceor lack of a relapse. A patient can be deemed responsive to a therapy ifrelapses do not occur. A response to therapy can also be determinedbased on a total number of relapses, a time to a first relapse, thepatient's EDSS score, a change in the patient's EDSS score (e.g., anincrease in the score corresponds to a lack of response to therapy), achange in MRI status (e.g., the development of additional lesions orplaques corresponds to a lack of response to therapy).

Patients can be clinically categorized in a level of disability. Forexample, the EDSS can be used to determine a severity of MS in apatient. Therefore, patients are categorized in categories thatcorrespond to an EDSS score between 1.0 and 10.0 in 0.5 point intervals.Generally, EDSS scores of 1.0 to 4.5 refer to patients with MS who areable to walk without any aid. EDSS scores of 5.0 to 9.5 refer topatients with MS whose ability to walk is impaired, with a higher scorecorresponding to a higher degree of impairment.

IV. Markers

In some embodiments, one or more markers are detected from a sampleobtained from an individual. The sample can be obtained by theindividual or by a third party, e.g., a medical professional. Examplesof medical professionals include physicians, emergency medicaltechnicians, nurses, first responders, psychologists, medical physicspersonnel, nurse practitioners, surgeons, dentists, and any otherobvious medical professional as would be known to one skilled in theart. The sample can be obtained from any bodily fluid, for example,amniotic fluid, aqueous humor, bile, lymph, breast milk, interstitialfluid, blood, blood plasma, cerumen (earwax), Cowper's fluid(pre-ejaculatory fluid), chyle, chyme, female ejaculate, menses, mucus,saliva, urine, vomit, tears, vaginal lubrication, sweat, serum, semen,sebum, pus, pleural fluid, cerebrospinal fluid, synovial fluid,intracellular fluid, and vitreous humour. In an example, the sample isobtained by a blood draw, where the medical professional draws bloodfrom a subject, such as by a syringe. The bodily fluid can then betested to determine values of one or more markers. The values of one ormore markers can be indicated as a numerical value. The numerical valuescan be obtained, for example, by experimentally obtaining measures froma sample obtained from an individual by an assay (e.g., an immunoassay)performed in a laboratory, or alternatively, obtaining a dataset from aservice provider such as a laboratory, or from a database or a server onwhich the dataset has been stored, e.g., on a storage memory.

In an embodiment, the quantity of one or more markers can be one or morequantitative expression values of: 6Ckine, Adiponectin, Adrenomedullin(ADM), Alpha-1 Antitrypsin (AAT), Alpha-1-Microglobulin (A1Micro),Alpha-2-Macroglobulin (A2Macro), Alpha-Fetoprotein (AFP), Amphiregulin(AR), Angiogenin, Angiopoietin 1 (ANG-1), Angiopoietin 2 (ANG-2),Angiotensin Converting Enzyme (ACE), Antileukoproteinase (ALP),Antithrombin III (ATIII), Apolipoprotein A (Apo-A), Apolipoprotein D(Apo-D), Apolipoprotein E (Apo-E), AXL Receptor Tyrosine Kinase (AXL),B-cell activating factor (BAFF), B Lymphocyte Chemoattractant (BLC),Beta-Amyloid (1-40) (AB-40), Beta-Amyloid (1-42) (AB-42), Beta-2Microglobulin (B2M), Betacellulin (BTC), Brain Derived NeurotrophicFactor (BDNF), C-Reactive Protein (CRP), Cadherin 1 (E-Cad), Calbindin,Cancer Antigen 125 (CA-125), Cancer Antigen 15-3 (CA 15-3), CancerAntigen 19-9 (CA 19-9), Carbonic anhydrase 9 (CA-9), CarcinoembryonicAntigen (CEA), Carcinoembryonic antigen related cell adhesion molecule 1(CEACAM1), Cathepsin D, CD40 Ligand (CD40-L), CD163, Ceruloplasmin,Chemokine CC-4 (HCC-4), Chromogranin A (CgA), Ciliary NeurotrophicFactor (CNTF), Clusterin (CLU), Complement C3 (C3), Complement Factor H(CFH), Complement Factor H Related Protein 1 (CFHR1), Cystatin B,CystatinC, Decorin, Dickkopf related protein 1 (DKK-1), Dopamine betahydroxylase (DBH), E-Selectin, EN-RAGE, Eotaxin-1, Eotaxin-2, Eotaxin-3,Epidermal Growth Factor (EGF), Epidermal Growth Factor Receptor (EGFR),Epiregulin (EPR), Epithelial Derived Neutrophil Activating Protein 78(ENA-78), Erythropoietin (EPO), Factor VII, Fas Ligand (FasL), FASLGReceptor (FAS), Ferritin (FRTN), Fibrinogen, Fibulin 1C (Fib1C), Ficolin3, Follicle Stimulating Hormone (FSH), Gastric inhibitory polypeptide(GIP), Gelsolin, Glucagon Like Peptide-1 (GLP-1), Glycogen phosphorylaseisoenzyme BB (GPBB), Granulocyte Colony Stimulating Factor (GCSF),Granulocyte Macrophage Colony Stimulating Factor (GM-CSF), Growthdifferentiation factor 15 (GDF-15), Growth Hormone (GH), GrowthRegulated alpha protein (GROalpha), Haptoglobin, Heat Shock protein 70(HSP-70), Heparin Binding EGF Like Growth Factor (HB-EGF), HepatocyteGrowth Factor (HGF), Human Chorionic Gonadotropin beta (hCG),Immunoglobulin A (IgA), Immunoglobulin E (IgE), Immunoglobulin M (IgM),Insulin, Insulin like Growth Factor Binding Protein 2 (IGFBP2),Intercellular Adhesion Molecule 1 (ICAM-1), Interferon alpha(IFN-alpha), Interferon gamma (IFN-gamma), Interferon gamma InducedProtein 10 (IP-10), Interferon inducible T cell alpha chemoattractant(ITAC), Interleukin 1 alpha (IL-1alpha), Interleukin 1 beta (IL-1beta),Interleukin 1 receptor antagonist (IL1ra), Interleukin 2 (IL-2),Interleukin 2 receptor alpha (IL2receptoralpha), Interleukin 3 (IL-3),Interleukin 4 (IL-4), Interleukin 5 (IL-5), Interleukin 6 (IL-6),Interleukin 6 receptor (IL6r), Interleukin 6 receptor subunit beta(IL6Rbeta), Interleukin 7 (IL-7), Interleukin 8 (IL-8), Interleukin 10(IL-10), Interleukin 12 Subunit p40 (IL12p40), Interleukin 12 Subunitp70 (IL12p70), Interleukin 13 (IL13), Interleukin 15 (IL15), Interleukin16 (IL16), Interleukin 17 (IL17), Interleukin 18 (IL18), Interleukin 18binding protein (IL18bp), Interleukin 22 (IL22), Interleukin 23 (IL23),Interleukin 31 (IL31), Kidney Injury Molecule 1 (KIM-1), Lactoferrin(LTF), Latency Associated Peptide of Transforming Growth Factor beta 1(LAP TGF b1), Leptin, Leptin Receptor (Leptin R), Leucine rich alpha 2glycoprotein (LRG1), Luteinizing Hormone (LH), Macrophage ColonyStimulating Factor 1 (M-CSF), Macrophage Derived Chemokine (MDC),Macrophage Inflammatory Protein 1 alpha (MIP1-alpha), MacrophageInflammatory Protein 1 beta (MIP1-beta), Macrophage Inflammatory Protein3 alpha (MIP3-alpha), Macrophage Inflammatory Protein 3 beta(MIP3-beta), Macrophage Migration Inhibitory Factor (MIF), MacrophageStimulating Protein (MSP), Mast stem cell growth factor receptor (SCFR),Matrix Metalloproteinase 1 (MMP-1), Matrix Metalloproteinase 2 (MMP-2),Matrix Metalloproteinase 3 (MMP-3), Matrix Metalloproteinase 7 (MMP-7),Matrix Metalloproteinase 9 (MMP-9), Matrix Metalloproteinase 9 total(MMP-9 total), Matrix Metalloproteinase 10 (MMP-10), Microalbumin,Monocyte Chemotactic Protein 1 (MCP-1), Monocyte Chemotactic Protein 2(MCP-2), Monocyte Chemotactic Protein 3 (MCP-3), Monocyte ChemotacticProtein 4 (MCP-4), Monokine Induced by Gamma Interferon (MIG), MyeloidProgenitor Inhibitory Factor 1 (MPIF-1), Myeloperoxidase (MPO),Myoglobin, Nerve Growth Factor beta (NGF-beta), Neurofilament heavypolypeptide (NF-H), Neuron Specific Enolase (NSE), Neuronal CellAdhesion Molecule (NrCAM), Neuropilin-1, Neutrophil Activating Peptide 2(NAP-2), Omentin, Osteocalcin, Osteopontin, Osteoprotegerin (OPG),P-Selectin, Pancreatic Polypeptide (PPP), Pancreatic secretory trypsininhibitor (TATI), Paraoxonase-1 (PON1), Pepsinogen-I (PGI), Periostin,Pigment Epithelium Derived Factor (PEDF), Placenta Growth Factor (PLGF),Plasminogen Activator Inhibitor 1 (PAI-1), Platelet endothelial celladhesion molecule (PECAM-1), Platelet Derived Growth Factor BB(PDGF-BB), Prolactin (PRL), Prostate Specific Antigen Free (PSA-f),Protein DJ-1 (DJ-1), Pulmonary and Activation Regulated Chemokine(PARC), Pulmonary surfactant associated protein D (SP-D), Receptor foradvanced glycosylation end products (RAGE), Resistin, S100 calciumbinding protein B (S100B), Serum Amyloid A Protein (SAA), Serum AmyloidP Component (SAP), Sex Hormone Binding Globulin (SHBG), Sortilin, ST2,Stem Cell Factor (SCF), Stromal cell derived factor 1 (SDF-1),Superoxide Dismutase 1 soluble (SOD-1), T Cell Specific Protein RANTES(RANTES), T Lymphocyte Secreted Protein I 309 (I309), Tamm HorsfallUrinary Glycoprotein (THP), Tenascin C (TN-C), Tetranectin, ThrombinActivatable ibrinolysis (TAFI), Thrombospondin-1, Thymus and activationregulated chemokine (TARC), Thyroid Stimulating Hormone (TSH), ThyroxineBinding Globulin (TBG), Tissue Inhibitor of Metalloproteinases 1(TIMP-1), Tissue Inhibitor of Metalloproteinases 2 (TIMP-2), TNF RelatedApoptosis Inducing Ligand Receptor 3 (TRAIL-R3), Transferrin receptorprotein 1 (TFR1), Transforming Growth Factor beta 3 (TGF-beta3), TumorNecrosis Factor alpha (TNF-alpha), Tumor Necrosis Factor beta(TNF-beta), Tumor necrosis factor ligand superfamily member 12 (Tweak),Tumor necrosis factor ligand superfamily member 13 (APRIL), TumorNecrosis Factor Receptor I (TNF-RI), Tumor necrosis factor receptor 2(TNFR2), Vascular Cell Adhesion Molecule 1 (VCAM-1), VascularEndothelial Growth Factor (VEGF), Visceral adipose tissue derived serpinA12 (Vaspin), Visfatin, Vitamin D Binding Protein (VDBP), Vitronectin,von Willebrand Factor (vWF), or YKL-40, resulting from evaluation of asample. Markers can also include those listed in the Tables and Figures.

In an embodiment, a marker's quantitative expression value can beincluded in a dataset associated with a sample obtained from a subject.In various embodiments, a dataset includes quantitative expressionvalues of two markers. In some embodiments, the dataset includesquantitative expression values of three, four, five, six, seven, eight,nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen,seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two,twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven,twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three,thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight,thirty-nine, forty, sixty, or eighty markers. As an example, a datasetcan include the expression values for PON1, Myoglobin, PAI1, TIMP1,SDF1, IL6Rbeta, Cystatin B, IgE, MIP3beta, and VCAM1. Other combinationsare described in more detail in the Examples section below.

In an embodiment, one or more markers can be divided into sets. Forexample, markers may be partitioned into a set 1, set 2, set 3, set 4,and set 5. In other examples, markers may be partitioned into more orfewer sets. In various embodiments, the sets are ranked according to theimportance of the markers in each set for predicting multiple sclerosisactivity in an individual. For example, markers in set 1 are rankedhigher than biomarkers in set 2, markers in set 2 are ranked higher thanmarkers in set 3, markers in set 3 are ranked higher than markers in set4, and markers in set 4 are ranked higher than markers in set 5. In anembodiment, a set can include one or more, two or more, three or more,four or more, five or more, six or more, seven or more, eight or more,nine or more, ten or more, eleven or more, twelve or more, thirteen ormore, fourteen or more, fifteen or more, sixteen or more, seventeen ormore, eighteen or more, nineteen or more, twenty or more, twenty-one ormore, twenty-two or more, twenty-three or more, twenty-four or more,twenty-five or more, twenty-six or more, twenty-seven or more,twenty-eight or more, twenty-nine or more, or thirty or more marker(s).

In some embodiments, set 1 and set 2 each include 10 biomarkers, whereasset 3, set 4, and set 5 each include 20 biomarkers. Specifically, set 1may include the markers of PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta,Cystatin B, IgE, MIP3beta, and VCAM1. Set 2 may include the markers ofMDC, VEGF, Ficolin 3, IgA, Factor VII, IL6R, RAGE, FIB1C, ITAC, and GH.Set 3 may include the markers of HBEGF, NrCAM, GROalpha, GDF15, SCFR,Ecad, Angiogenin, Sortilin, AAT, IgM, PARC, SP-D, BAFF, ADM, PEDF,IL1ra, TBG, Microalbumin, Leptin, and Eotaxin 2. Set 4 may include themarkers of IGFBP2, Resistin, Cathepsin D, E-Selectin, YKL40, IL22, IL8,CA 15-3, LeptinR, IGFBP2, MCP1, PRL, Tetranectin, CEACAM1, 6Ckine, SAP,CFHR1, HCC-4, and C3. Set 5 may include the markers of AFP, ANG-1, IL18,Gelsolin, TN-C, Vitronectin, B2M, TATI, MMP3, Omentin, IL 18bp, ApoD,MCP-4, Apo-E, ST2, Thrombospondin 1, GIP, MMP7, ICAM-1, and DKK1.

V. Assays

Examples of assays for one or more markers include DNA assays,microarrays, polymerase chain reaction (PCR), RT-PCR, Southern blots,Northern blots, antibody-binding assays, enzyme-linked immunosorbentassays (ELISAs), flow cytometry, protein assays, Western blots,nephelometry, turbidimetry, chromatography, mass spectrometry,immunoassays, including, by way of example, but not limitation, RIA,immunofluorescence, immunochemiluminescence,immunoelectrochemiluminescence, or competitive immunoassays,immunoprecipitation, and the assays described in the Examples sectionbelow. The information from the assay can be quantitative and sent to acomputer system of the invention. The information can also bequalitative, such as observing patterns or fluorescence, which can betranslated into a quantitative measure by a user or automatically by areader or computer system. In an embodiment, the individual can alsoprovide information other than assay information to a computer system,such as race, height, weight, age, gender, eye color, hair color, familymedical history and any other information that may be useful forpredicting multiple sclerosis activity in the individual.

Various immunoassays designed to quantitate markers can be used inscreening including multiplex assays. Measuring the concentration of atarget marker in a sample or fraction thereof can be accomplished by avariety of specific assays. For example, a conventional sandwich typeassay can be used in an array, ELISA, RIA, etc. format. Otherimmunoassays include Ouchterlony plates that provide a simpledetermination of antibody binding. Additionally, Western blots can beperformed on protein gels or protein spots on filters, using a detectionsystem specific for the markers as desired, conveniently using alabeling method.

Protein based analysis, using an antibody as described above thatspecifically binds to a polypeptide (e.g. marker), can be used toquantify the marker level in a test sample obtained from an individual.For multiplex analysis of markers, arrays containing one or more markeraffinity reagents, e.g. antibodies can be generated. Such an array canbe constructed comprising antibodies against markers. Detection canutilize one or a panel of marker affinity reagents, e.g. a panel orcocktail of affinity reagents specific for one, two, three, four, fiveor more markers.

VI. Prediction Model

V.A. System Overview

FIG. 1 depicts an overview of an environment 100 for assessing MSactivity in an individual, in accordance with an embodiment. Theenvironment 100 provides context in order to introduce a markerquantification assay 140 and an activity prediction system 160.

The marker quantification assay 140 determines quantitative expressionvalues of one or more biomarkers from a test sample obtained from theindividual 110. As described above, the assay 140 may be an immunoassay,and more specifically, a multi-plex immunoassay. Therefore, theexpression levels of various biomarkers can be obtained in a single runusing a single test sample obtained from the individual 110. Thequantified expression values of the biomarkers are provided to theactivity prediction system 160.

The activity prediction system 160 includes one or more computer models,embodied in a computer 600 as discussed below with respect to FIG. 6,which analyzes the received biomarker expression values to generate anassessment of MS activity 150 in the individual 110. Reference is nowmade to FIG. 2 which depicts a block diagram illustrating the computerlogic components of the activity prediction system 160, in accordancewith an embodiment. Specifically, the activity prediction system 160 mayinclude a model assembly module 212, a training module 214, a modelapplication module 216, a prediction module 218, as well as a trainingdata store 230 and a prediction model store 240.

Each of the components of the activity prediction system 160 ishereafter described in reference to two phases: 1) a training phase and2) an execution phase. More specifically, the training phase refers tothe building and training of one or more prediction models based ontraining data that includes quantitative expression values of biomarkersobtained from individuals with known MS activity. Therefore, the one ormore prediction models are trained to predict MS activity in anindividual based on quantitative biomarker expression values. During theexecution phase, a prediction model can be applied quantitativebiomarker expression values from a test sample obtained from anindividual of interest in order to generate a prediction of MS activityin the individual of interest.

In some embodiments, the components of the activity prediction system160 are applied during one of the training phase and the executionphase. For example, the model assembly module 212 and training module214 (dotted lines) are applied during the training phase whereas themodel application module 216 and prediction module 218 (solid lines) areapplied during the execution phase.

V.B. Building and Training a Prediction Model

During the training phase, the model assembly module 212 builds one ormore predictive models based on expression values of biomarkers. Invarious embodiments, the model assembly module 212 adjusts thequantitative expression values of each biomarker prior to building thepredictive models. For example, the quantitative expression values ofeach biomarker may be adjusted according to the age, the gender, orother personal characteristics of the individual from whom the samplewas obtained. The variable effects of different personal characteristicscan be mitigated in order to produce a prediction model that reflectsthe influence of individual biomarker values on the predicted output.

To identify a set of biomarkers that are to be used to build a model,the model assembly module 212 may begin with a list of candidatebiomarkers that may be deemed promising for predicting MS activity in anindividual. In one embodiment, candidate biomarkers may be biomarkersidentified through a literature curation process. In some embodiments,candidate biomarkers may be biomarkers whose expression values in testsamples obtained from individuals that are positive for MS activity(e.g., presence of MS, in an exacerbated state, and the like) arestatistically significant in comparison to expression values ofbiomarkers in test samples obtained from individuals that are negativefor the MS activity. As an example, a total of 215 candidate biomarkersare shown in Table 2 below.

In one embodiment, the model assembly module 212 builds a predictivemodel that considers the expression values of all candidate biomarkers.In another embodiment, the model assembly module 212 partitions thecandidate biomarkers and constructs a predictive model from a subset ofthe candidate biomarkers. As an example, the model assembly module 212can rank the candidate biomarkers based on their importance and select asubset of candidate biomarkers for building the predictive model giventhe rankings. For example, candidate biomarkers that are determined tobe highly correlated with MS activity would be deemed highly importantand highly ranked. In one embodiment, the model assembly module 212selects biomarkers above a threshold ranking to be used in constructingthe predictive model.

In some embodiments, the importance of each candidate biomarker isdetermined by using a method including one of random forest (RF),gradient boosting (GBM), extreme gradient boosting (XGB), or LASSOalgorithms. For example, if using random forest algorithms, the modelassembly module 212 may generate a variable importance plot that depictsthe importance of each candidate biomarker. Specifically, the randomforest algorithm may provide, for each candidate biomarker, 1) a meandecrease in model accuracy and 2) a mean decrease in a Gini coefficientwhich is a measure of how much each candidate biomarker contributes tothe homogeneity of nodes and leaves in the random forest. In onescenario, the importance of each candidate biomarker is dependent on oneor both of the mean decrease in model accuracy and mean decrease in Ginicoefficient. Each of GBM, XGB, and LASSO, can also be used to rank theimportance of each candidate biomarker based on an influence value, asdescribed below in the Examples. Therefore, the model assembly module212 can generate a ranking of each of candidate biomarkers using one ofthe methods including RF, GBM, XGB, or LASSO.

In some embodiments, in generating a ranking of candidate biomarkersusing an analysis method, the model assembly module 212 generatesrankings in an iterative fashion. As an example, the model assemblymodule 212 may generate an initial ranking of N candidate biomarkers.The model assembly module 212 may fix the rankings of candidatebiomarkers ranked below a threshold and re-ranks the candidatebiomarkers above the threshold. The model assembly module 212 mayiterate this process. For example, if N=215 candidate biomarkers, themodel assembly module 212 fixes the ranking of candidate biomarkers fromrank 81 to rank 215 while re-ranking biomarkers ranked in the top 80. Invarious embodiments, a candidate biomarker may be ranked significantlydifferently in the first ranking in comparison to its re-ranking. In thenext iteration, reranked candidate biomarkers between ranks 61 and 80are fixed while candidate biomarkers in the top 60 are reranked. Thisiterative ranking process may continue for the top 40, top 20, top 10,top 5, and top 2 candidate biomarkers.

In various embodiments, in order to minimize bias in a ranking ofcandidate biomarkers from a single analysis method, the model assemblymodule 212 can generate a ranking of candidate biomarkers by combiningrankings generated by multiple analysis methods. For example, the modelassembly module 212 may generate a first ranking of candidate biomarkersusing RF, a second ranking of candidate biomarkers using GBM, a thirdranking of candidate biomarkers using XGB, and a fourth ranking ofcandidate biomarkers using LASSO. In one embodiment, the final rankingof biomarkers can be an average of the ranking of each biomarker fromtwo, three, four, or more different rankings. In some embodiments,additional rankings using additional or the same analysis methods may begenerated. The final ranking of candidate biomarkers may be dependent onthe ranking of each candidate biomarker in each of the first, second,third, and fourth rankings. In one embodiment, each of the first,second, third, and fourth rankings are assigned weights that areconsidered in generating the final rankings of candidate biomarkers. Forexample, the final rankings may be a weighted sum of the rankings ofbiomarkers from two, three, four, or more different rankings.

The model assembly module 212 constructs one or more predictive models,each predictive model receiving, as input, one or more biomarkers. Inone embodiment, the one or more biomarkers are selected from a rankingof the candidate biomarkers. As one example, the model assembly module212 constructs a predictive model that receives, as input, twobiomarkers that are ranked in the top 80 of the candidate biomarkers.

In some embodiments, the model assembly module 212 constructs apredictive model for more than two biomarkers. For example, a predictivemodel is constructed to receive, as input, the top 80 biomarkers in theranking. In some embodiments, the model assembly module 212 constructs apredictive model for the top 60, top 40, top 20, or top 10 biomarkers inthe ranking. In further embodiments, the model assembly module 212constructs a predictive model for the top 2 biomarkers in the ranking.

The model assembly module 212 may provide the constructed predictivemodels to the training module 214 to train each of the predictivemodels.

The training module 214 trains each of the predictive models usingtraining data that is stored in the training data store 230. In someembodiments, the training module 214 retrieves the training data andrandomly partitions the training data into a training set and avalidation set. As an example, 80% of the training data may bepartitioned into the training set and the other 20% can be partitionedinto the validation set. Other proportions of training set andvalidation set may be implemented. As such, the training set is used totrain the predictive models whereas the validation set is used tovalidate the predictive models.

Reference is now made to FIG. 3, which illustrates an example set oftraining data 230A, in accordance with an embodiment. As shown in FIG.3, the training data 230A may include data corresponding to multipleindividuals (e.g., column 1 depicting individual 1, 2, 3, 4 . . . ). Foreach individual, the training data 230A includes quantitative expressionvalues of different markers from a test sample obtained from theindividual. In some embodiments, the quantitative expression values aredetermined by the marker quantification assay 140 shown in FIG. 1.Although FIG. 3 depicts 4 individuals and 2 different markers (marker Aand marker B), the training data 230A may include tens, hundreds, orthousands of individuals as well as tens, hundreds, or thousands ofmarkers.

As depicted in FIG. 3, a test sample obtained from individual 1 cancorrespond to a quantified expression value of “A1” for marker A, aquantified expression value of “B1” for marker B, and so on. Similarly,a test sample obtained from individual 2 can correspond to a quantifiedexpression value of “A2” for marker A, a quantified expression value of“B2” for marker B, and so on. Individuals 3 and 4 have correspondingmarker values as shown in FIG. 3.

The training data 230A can also include a positive or negativeindication as to MS activity in each individual. Each indication may bea clinical result (e.g., a clinical diagnosis) that has classified thepatient in a category, as described above in the section entitled“Multiple Sclerosis Activity in an Individual.” For example, if MSactivity corresponds to the presence of MS in individual 1, a positiveresult can correspond to a positive diagnosis of MS in individual 1based on an MRI image obtained from individual 1. Similarly, anindication of a negative result (e.g., individual 3 or individual 4)corresponds to a negative diagnosis of MS in the individual based on acorresponding MRI image.

In various embodiments, the training data 230A may further includepersonal characteristics of the individual (e.g., sex, age, and thelike). Therefore, in some embodiments, the quantitative expressionvalues of biomarkers in the training data 230A can be adjusted based onthe personal characteristics of the individual.

The training module 214 may identify model parameters that are thentuned during training to optimize the performance (e.g., minimizeprediction error) of each predictive model. As an example, the trainingmodule 214 identifies model parameters using a cross validation process.In some embodiments, the cross validation may be a 10-fold 5-repeatcross validation process. Specifically, the training module 214 mayretrieve a portion of the training set and further partition the portioninto 10 subsamples such that 9 subsamples are used to train and identifymodel parameters. The training module 214 may repeat the subsampling 5times to minimize the impact of randomness that may arise due tosubsampling. The one holdout subsample is used to generate a measure ofmodel performance given the identified model parameters. Therefore,given the average model performance across the multiple cross validationruns, the best model parameters are selected to be further tuned throughthe training phase.

Each predictive model is iteratively trained using, as input, thequantitative expression values of the markers for each individual. Forexample, one iteration involves providing input training data thatincludes the quantitative expression value A1 for Marker A fromindividual 1, quantitative expression value A2 for Marker B fromindividual 1, and so on. Each predictive model can be trained on groundtruth data that includes the indication (e.g., the positive or negativeresult). Over training iterations, each predictive model is trained(e.g., the parameters are tuned) to minimize a prediction error betweena prediction of MS activity outputted by the predictive model and theground truth data.

The training module 214 can further validate each prediction model usingthe validation set (e.g., 20% of training data). For each samplecorresponding to an individual in the validation set, the predictionmodel outputs a prediction of MS activity in the individual. Theprediction is a quantitative measure of a relative likelihood that thesample belongs to one of two classes in question. The quantitativemeasure is used to compute the area under the curve (AUC), or themeasure of the model's overall performance across the validation set.The trained predictive models are stored in the prediction model store240 such that they can be appropriately retrieved at a subsequent time(e.g., execution phase).

V.C. Applying a Prediction Model

Returning to FIG. 2, during the execution phase, the marker applicationmodule 216 receives quantitative biomarker expression values from a testsample obtained from an individual of interest. As an example, theindividual may not have been previously diagnosed with MS and therefore,the marker application module 216 retrieves and applies a trainedprediction model to determine whether the individual to be diagnosedwith MS based on the quantitative biomarker expression values.

Specifically, the model application module 216 retrieves the appropriateprediction model from the prediction model store 240. For example, theprediction model store 240 may include prediction models that predictdifferent types of MS activity (e.g., positive/negative diagnosis of MS,a state of MS (e.g., quiescent vs exacerbated), a therapeutic response,a positive identification of a flare or relapse of MS, and the like).Therefore, the model application module 216 may identify the appropriateprediction model and applies the prediction model to generate theassessment of MS activity in the individual. The assessment of MSactivity can be presented on a display, such as the display 618 depictedin FIG. 6 of an example computer 600. The assessment of MS activity inthe individual may be informative for determining a course of treatmentfor the individual.

In various embodiments, the assessment of MS activity is a predictedscore outputted by the prediction model. The score may be informative ofthe MS activity in the individual.

In one embodiment, the MS activity corresponds to the presence orabsence of multiple sclerosis in an individual. Therefore, theassessment (e.g., predicted score) provided by the prediction model canbe used to assess the MS activity. As an example, the assessment can beused to determine whether the individual is to be diagnosed with MS. Invarious embodiments, the assessment (e.g., predicted score)corresponding to the individual is compared to a distribution ofpredicted scores obtained from the prediction model that correspond tohealthy patients (e.g., not clinically diagnosed with MS). In thisscenario, the individual is positive for MS activity (e.g., diagnosedwith MS) if the individual's predicted score is significantly different(e.g., p-value <0.05) in comparison to the distribution of predictedscores of healthy patients. In various embodiments, the individual canbe subsequently treated for MS. In other words, the assessment can guidethe treatment of the individual.

In one embodiment, the MS activity corresponds to a state (e.g.,quiescent vs exacerbation) of MS in an individual. Therefore, theassessment (e.g., predicted score) provided by the prediction model canbe used to assess the MS activity. In various embodiments, theassessment (e.g., predicted score) corresponding to the individual iscompared to one or two distributions of predicted scores obtained fromthe prediction model. For example, a first distribution of predictedscores may correspond to individuals previously determined to be in aquiescent state (e.g., clinically determined to be in a quiescentstate). A second distribution of predicted scores may correspond toindividuals previously determined to be in an exacerbated state (e.g.,clinically determined to be in an exacerbated state). In this scenario,the individual may be classified as being in a quiescent state if theindividual's predicted score is not significantly different (e.g.,p-value >0.05) than the distribution of predicted scores correspondingto individuals previously determined to be in a quiescent state.Alternatively, the individual may be classified as being in anexacerbated state if the individual's predicted score is notsignificantly different (e.g., p-value >0.05) than the distribution ofpredicted scores corresponding to individuals previously determined tobe in an exacerbated state. In various embodiments, the subsequenttreatment of an individual previously diagnosed with MS can be tailoreddepending on the predicted state of MS in the individual.

In one embodiment, the MS activity corresponds to a response to atherapy of an individual diagnosed with multiple sclerosis. Therefore,the assessment (e.g., predicted score) provided by the prediction modelcan be used to assess the MS activity. In various embodiments, theassessment (e.g., predicted score) corresponding to the individual iscompared to one or two distributions of predicted scores obtained fromthe prediction model. For example, a first distribution of predictedscores may correspond to individuals previously determined to beresponsive to the therapy (e.g., clinically determined to be responsiveto the therapy). A second distribution of predicted scores maycorrespond to individuals previously determined to be non-responsive tothe therapy (e.g., clinically determined to be non-responsive to thetherapy). In this scenario, the individual may be classified asresponsive to a therapy if the individual's predicted score is notsignificantly different (e.g., p-value >0.05) than the distribution ofpredicted scores corresponding to individuals previously determined tobe responsive to the therapy. Alternatively, the individual may beclassified as non-responsive to a therapy if the individual's predictedscore is not significantly different (e.g., p-value >0.05) than thedistribution of predicted scores corresponding to individuals previouslydetermined to be non-responsive to the therapy. In various embodiments,the subsequent treatment of the individual can be tailored depending onthe predicted responsiveness or non-responsiveness to the therapy. Forexample, the dosing regimen (e.g., time or dose quantity) can bealtered. As another example, a different therapy may be provided.

In one embodiment, the MS activity corresponds to a degree of MSdisability in an individual diagnosed with multiple sclerosis. In oneembodiment, the degree of MS disability corresponds to the EDSS.Therefore, the assessment (e.g., predicted score) provided by theprediction model can be used to assess the MS activity. In variousembodiments, the assessment (e.g., predicted score) corresponding to theindividual is compared to multiple distributions of predicted scoresobtained from the prediction model. Each distribution of predictedscores may correspond to a group of individuals that have beenclinically categorized in a degree of disability. For example, a firstdistribution of predicted scores may correspond to individualsclinically categorized with a score of 1 on the EDSS. Additionaldistributions of predicted scores may correspond to groups ofindividuals that have been clinically categorized with a score of 1.5,2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5,9.0, 9.5, and 10.0. In one scenario, the individual may be classifiedwith one of the EDSS scores if the individual's predicted score is notsignificantly different (e.g., p-value >0.05) from one group and issignificantly different (e.g., p-value <0.05) in comparison to all othergroups. The individual may be treated according to clinical protocolsbased on the categorization.

In one embodiment, the MS activity corresponds to a risk (e.g.,likelihood) of the individual developing MS at a subsequent time.Therefore, the assessment (e.g., predicted score) provided by theprediction model can be used to assess the MS activity. In variousembodiments, the assessment (e.g., predicted score) corresponding to theindividual is compared to multiple distributions of predicted scores.Each distribution of predicted scores may correspond to a group ofindividuals in a risk group that have been clinically categorized with aparticular risk of developing MS. As an example, the risk groups may bedivided into a high risk group, medium risk group, and low risk group.In one scenario, the individual may be classified in a risk group if theindividual's predicted score is not significantly different (e.g.,p-value >0.05) from one group and is significantly different (e.g.,p-value <0.05) in comparison to other groups. Therefore, the individualcan undertake changes in lifestyle and/or treatments based on theprediction of a risk/likelihood of developing MS.

VII. Therapeutic Agents and Compositions for Therapeutic Agents

In one embodiment of the invention, a therapeutic agent is provided toan individual prior to and/or subsequent to obtaining the sample fromthe individual and determining quantitative expression values of one ormore markers in the obtained sample. As one example, a predictive modelthat receives the quantitative expression values predicts that anindividual is to be diagnosed with multiple sclerosis and a therapeuticagent is to be provided. In another example, the predictive modelpredicts that a provided therapeutic agent is demonstrating therapeuticefficacy against a multiple in a previously diagnosed individual.

In various embodiments the therapeutic agent is a biologic, e.g. acytokine, antibody, soluble cytokine receptor, anti-senseoligonucleotide, siRNA, etc. Such biologic agents encompass muteins andderivatives of the biological agent, which derivatives can include, forexample, fusion proteins, PEGylated derivatives, cholesterol conjugatedderivatives, and the like as known in the art. Also included areantagonists of cytokines and cytokine receptors, e.g. traps andmonoclonal antagonists, e.g. IL-1Ra, IL-1 Trap, sIL-4Ra, etc. Alsoincluded are biosimilar or bioequivalent drugs to the active agents setforth herein.

Therapeutic agents for multiple sclerosis include corticosteroids,plasma exchange, ocrelizumab (Ocrevus®), IFN-3 (Avonex®, Betaseron®,Rebif®), Glatiramer acetate (Copaxone®), anti-VLA4 (Tysabri,natalizumab), dimethyl fumarate (Tecfidera®), teriflunomide (Aubagio®),fingolimod (Gilenya®), anti-CD52 antibody (e.g., alemtuzumab),methotrexate, cladribine, simvastatin, and cyclophosphamide. In additionor alternative to therapeutic agents, other treatments for multiplesclerosis include lifestyle changes such as physical therapy or a changein diet. The method also provide for combination therapy of one or moretherapeutic agents and/or additional treatments, where the combinationcan provide for additive or synergistic benefits.

A pharmaceutical composition administered to an individual includes anactive agent such as the therapeutic agent described above. The activeingredient is present in a therapeutically effective amount, i.e., anamount sufficient when administered to treat a disease or medicalcondition mediated thereby. The compositions can also include variousother agents to enhance delivery and efficacy, e.g. to enhance deliveryand stability of the active ingredients. Thus, for example, thecompositions can also include, depending on the formulation desired,pharmaceutically-acceptable, non-toxic carriers or diluents, which aredefined as vehicles commonly used to formulate pharmaceuticalcompositions for animal or human administration. The diluent is selectedso as not to affect the biological activity of the combination. Examplesof such diluents are distilled water, buffered water, physiologicalsaline, PBS, Ringer's solution, dextrose solution, and Hank's solution.In addition, the pharmaceutical composition or formulation can includeother carriers, adjuvants, or non-toxic, nontherapeutic, nonimmunogenicstabilizers, excipients and the like. The compositions can also includeadditional substances to approximate physiological conditions, such aspH adjusting and buffering agents, toxicity adjusting agents, wettingagents and detergents. The composition can also include any of a varietyof stabilizing agents, such as an antioxidant.

The pharmaceutical compositions described herein can be administered ina variety of different ways. Examples include administering acomposition containing a pharmaceutically acceptable carrier via oral,intranasal, rectal, topical, intraperitoneal, intravenous,intramuscular, subcutaneous, subdermal, transdermal, intrathecal, orintracranial method.

Such a pharmaceutical composition may be administered for prophylactic(e.g., before diagnosis of a patient with multiple sclerosis) or fortreatment (e.g., after diagnosis of a patient with multiple sclerosis)purposes. Preventing, prophylaxis or prevention of a disease or disorderas used in the context of this invention refers to the administration ofa composition to prevent the occurrence or onset of multiple sclerosisor some or all of the symptoms of multiple sclerosis or to lessen thelikelihood of the onset of a disease or disorder. Treating, treatment,or therapy of multiple sclerosis shall mean slowing, stopping orreversing the disease's progression by administration of treatmentaccording to the present invention. In the preferred embodiment,treating multiple sclerosis means reversing the disease's progression,ideally to the point of eliminating the disease itself.

VII. Computer Implementation

The methods of the invention, including the methods of assessingmultiple sclerosis activity in an individual, are, in some embodiments,performed on a computer.

For example, the building and execution of a predictive model anddatabase storage can be implemented in hardware or software, or acombination of both. In one embodiment of the invention, amachine-readable storage medium is provided, the medium comprising adata storage material encoded with machine readable data which, whenusing a machine programmed with instructions for using said data, iscapable of displaying any of the datasets and execution and results of apredictive model of this invention. Such data can be used for a varietyof purposes, such as patient monitoring, treatment considerations, andthe like. The invention can be implemented in computer programsexecuting on programmable computers, comprising a processor, a datastorage system (including volatile and non-volatile memory and/orstorage elements), a graphics adapter, a pointing device, a networkadapter, at least one input device, and at least one output device. Adisplay is coupled to the graphics adapter. Program code is applied toinput data to perform the functions described above and generate outputinformation. The output information is applied to one or more outputdevices, in known fashion. The computer can be, for example, a personalcomputer, microcomputer, or workstation of conventional design.

Each program can be implemented in a high level procedural or objectoriented programming language to communicate with a computer system.However, the programs can be implemented in assembly or machinelanguage, if desired. In any case, the language can be a compiled orinterpreted language. Each such computer program is preferably stored ona storage media or device (e.g., ROM or magnetic diskette) readable by ageneral or special purpose programmable computer, for configuring andoperating the computer when the storage media or device is read by thecomputer to perform the procedures described herein. The system can alsobe considered to be implemented as a computer-readable storage medium,configured with a computer program, where the storage medium soconfigured causes a computer to operate in a specific and predefinedmanner to perform the functions described herein.

The signature patterns and databases thereof can be provided in avariety of media to facilitate their use. “Media” refers to amanufacture that contains the signature pattern information of thepresent invention. The databases of the present invention can berecorded on computer readable media, e.g. any medium that can be readand accessed directly by a computer. Such media include, but are notlimited to: magnetic storage media, such as floppy discs, hard discstorage medium, and magnetic tape; optical storage media such as CD-ROM;electrical storage media such as RAM and ROM; and hybrids of thesecategories such as magnetic/optical storage media. One of skill in theart can readily appreciate how any of the presently known computerreadable mediums can be used to create a manufacture comprising arecording of the present database information. “Recorded” refers to aprocess for storing information on computer readable medium, using anysuch methods as known in the art. Any convenient data storage structurecan be chosen, based on the means used to access the stored information.A variety of data processor programs and formats can be used forstorage, e.g. word processing text file, database format, etc.

VI.A. Example Computer

FIG. 6 illustrates an example computer 600 for implementing the entitiesshown in FIGS. 1 and 3. The computer 600 includes at least one processor602 coupled to a chipset 604. The chipset 604 includes a memorycontroller hub 620 and an input/output (I/O) controller hub 622. Amemory 606 and a graphics adapter 612 are coupled to the memorycontroller hub 620, and a display 618 is coupled to the graphics adapter612. A storage device 608, an input device 614, and network adapter 616are coupled to the I/O controller hub 622. Other embodiments of thecomputer 600 have different architectures.

The storage device 608 is a non-transitory computer-readable storagemedium such as a hard drive, compact disk read-only memory (CD-ROM),DVD, or a solid-state memory device. The memory 606 holds instructionsand data used by the processor 602. The input interface 614 is atouch-screen interface, a mouse, track ball, or other type of pointingdevice, a keyboard, or some combination thereof, and is used to inputdata into the computer 600. In some embodiments, the computer 600 may beconfigured to receive input (e.g., commands) from the input interface614 via gestures from the user. The graphics adapter 612 displays imagesand other information on the display 618. The network adapter 616couples the computer 600 to one or more computer networks.

The computer 600 is adapted to execute computer program modules forproviding functionality described herein. As used herein, the term“module” refers to computer program logic used to provide the specifiedfunctionality. Thus, a module can be implemented in hardware, firmware,and/or software. In one embodiment, program modules are stored on thestorage device 608, loaded into the memory 606, and executed by theprocessor 602.

The types of computers 600 used by the entities of FIG. 1 can varydepending upon the embodiment and the processing power required by theentity. For example, the presentation identification system 160 can runin a single computer 600 or multiple computers 600 communicating witheach other through a network such as in a server farm. The computers 600can lack some of the components described above, such as graphicsadapters 612, and displays 618.

VI.B. Kit Implementation

Also disclosed herein are kits for assessing multiple sclerosis activityin an individual. Such kits can include reagents for detectingexpression levels of one or markers and instructions for assessingmultiple sclerosis activity based on the detected expression levels.

The detection reagents can be provided as part of a kit. Thus, theinvention further provides kits for detecting the presence of a panel ofspecific markers of interest in a biological sample. A kit can comprisea set of reagents for generating a dataset via at least one proteindetection assay that is associated with a sample from the individual.The set of reagents enable the detection of quantitative expressionlevels of one or more markers from set 1, set 2, set 3, set 4, and set 5as set forth in Table 3. In certain aspects, the reagents include one ormore antibodies that bind to one or more of the markers. The antibodiesmay be monoclonal antibodies or polyclonal antibodies. In some aspects,the reagents can include reagents for performing ELISA including buffersand detection agents.

A kit can include instructions for use of a set of reagents. Forexample, a kit can include instructions for performing at least onemarker detection assay such as an immunoassay, a protein-binding assay,an antibody-based assay, an antigen-binding protein-based assay, aprotein-based array, an enzyme-linked immunosorbent assay (ELISA), flowcytometry, a protein array, a blot, a Western blot, nephelometry,turbidimetry, chromatography, mass spectrometry, enzymatic activity, andan immunoassay selected from RIA, immunofluorescence,immunochemiluminescence, immunoelectrochemiluminescence,immunoelectrophoretic, a competitive immunoassay, andimmunoprecipitation.

In addition to the above components, the subject kits will furtherinclude instructions for practicing the subject methods. Theseinstructions can be present in the subject kits in a variety of forms,one or more of which can be present in the kit. One form in which theseinstructions can be present is as printed information on a suitablemedium or substrate, e.g., a piece or pieces of paper on which theinformation is printed, in the packaging of the kit, in a packageinsert, etc. Yet another means would be a computer readable medium,e.g., diskette, CD, hard-drive, network data storage, etc., on which theinformation has been recorded. Yet another means that can be present isa website address which can be used via the internet to access theinformation at a removed site. Any convenient means can be present inthe kits.

EXAMPLES

Below are examples of specific embodiments for carrying out the presentinvention. The examples are offered for illustrative purposes only, andare not intended to limit the scope of the present invention in any way.Efforts have been made to ensure accuracy with respect to numbers (e.g.,p-values, area under the curve) used but some experimental error anddeviation should, of course, be allowed for.

Example 1: Demographic Data

Baseline demographic characteristics of test samples obtained from 125individuals in the Accelerated Cure Project (ACP) registry are shown inTable 1.

Example 2: Feasibility of Assays in Determining Correlation BetweenExpression Values of Markers and Presence of MS

Blood serum samples were obtained from 8-12 individual samples from MSand healthy individuals. Samples obtained from MS individuals werepooled and samples obtained from healthy individuals were similarlypooled. Quantitative expression values of a total of 220 biomarkers wereassessed in the pooled MS samples and pooled healthy samples bymultiplex luminex analysis (Rules Based Medicine). FIG. 4 and FIG. 5illustrate the feasibility of correlating upregulated and downregulatedquantitative expression values of biomarkers with the presence of MS incomparison to the absence of MS. Specifically, FIG. 4 depicts the top 40identified biomarkers that are upregulated in MS patients in comparisonto healthy individuals. FIG. 5 depicts the top 40 identified biomarkersthat are downregulated in MS patients in comparison to healthyindividuals.

Example 3: Univariate Analysis of One Biomarker for Predicting aQuiescent or Exacerbated State of MS in an Individual

Univariate analyses was performed to obtain measures of potentialpredictive utility for each of 199 analytes. Table 2 depicts theunivariate analysis of single biomarkers for predicting a state (e.g.,quiescent or exacerbated) of MS in an individual. Specifically, table 2includes:

-   -   P-value for a T-test comparing the quantified level of a        biomarker determined from the Exacerbation subgroup (N=60) in        comparison to the Quiescent subgroup (N=65)    -   Predictive metric (e.g., Area under the curve (AUC)) of a        single-analyte logistic regression model

For the logistic regression model, quantitative expression values for abiomarker were fit to a logistic regression. The predictive ability ofthe logistic regression was evaluated using the fitted values andreported as the AUC metric.

These measures provide quantitative indications of each biomarker'sability to distinguish sample groups of interest (exacerbation vsquiescent). A p-value <0.05 is generally accepted as a statisticallysignificant threshold for identifying biomarkers that areupregulated/downregulated. An AUC value >0.60 is a reasonable thresholdfor identifying promising biomarkers using the univariate logisticregression model.

Example 4: Multivariate Analyses of Two Biomarkers for Predicting aQuiescent or Exacerbated State of MS in an Individual

Multivariate analyses were conducted to obtain measures of potentialpredictive utility for pairs of biomarkers. Specifically, pairs ofbiomarkers were selected from the ranked biomarker list of 80 totalbiomarkers shown in Table 3. The ranking of biomarkers is describedbelow in reference to Example 6.

For each pair of biomarkers, two predictive models were constructedusing the quantitative expression values of each pair of biomarkers. Thetwo predictive models include: 1) a logistic regression model and 2) arandom forest model. The predictive ability of each model was evaluatedusing the fitted expression values and reported as an AUC metric. Foreach model, an AUC greater than a threshold value of 0.6 is consideredpredictive. The evaluated predictive ability of each predictive modelbased on a pair of biomarkers is described in further detail below inreference to Examples 9-87.

Example 5: Multivariate Analysis of 10/20/40/60/80 Biomarkers forPredicting a Quiescence or Exacerbated State of MS in an Individual

Multivariate analyses were conducted to obtain measures of potentialpredictive utility for N biomarkers shown in Table 3, where N biomarkersis one of 10, 20, 40, 60, or 80 total biomarkers. Overall, themultivariate analyses included ranking candidate biomarkers based oneach candidate biomarker's importance as discussed in Example 6 below.Predictive models using one of random forest (RF), gradient boosting(GBM), extreme gradient boosting (GBM), or least absolute shrinkage andselection operator (LASSO) were built including biomarkers that wereselected based on their respective rankings. Each predictive model wastrained using as is discussed in Example 7 below. Each predictive modelwas validated through the reporting of a mean AUC metric as described inExample 8 below.

Example 6: Assessing Analyte Importance

For each of the four methods (random forest (RF), gradient boosting(GBM), least absolute shrinkage and selection operator (LASSO), andextreme gradient boosting (XGB)), two independent model building runswere executed to determine analyte importance (ranking). Eachindependent model building run iteratively identified the Top80/60/40/20/10 biomarkers.

Each of the multivariate classification methods (RF, GBM, LASSO, andXGB) provides one or more quantitative measures for assessing andranking the importance of every biomarker (variable) included in a modelproduced by that method. For example, RF provides, for each variable:(a) the mean decrease in model accuracy, and (b) the mean decrease inGini index, which is a measure of variable importance based on nodepurity at node splits involving the variable in question as the trees ofthe random forest model are being built. These method-specific measuresare used to rank analytes by importance.

For each method, the biomarkers are iteratively trimmed to the top80/60/40/20/10. At each iteration, a new model is built using theshorter list of biomarkers. In other words, we first build a model usingall analytes and select the Top 80 ranked by the method. Then we build amodel using only the Top 80 and we select the Top 60 (which could differin composition from the 60 top analytes in the first model). Then webuild a model using only the Top 60 and we select the Top 40. Then webuild a model using only the Top 40 and we select the Top 20. Then webuild a model using only the Top 20 and we select the Top 10.

Each method independently generates a ranking of top biomarkers. Aftercomputing the Top N biomarker lists for each of the methods, thebiomarkers are sorted by a number of Top N appearances (e.g., totalnumber of times an analyte is ranked in the Top 80/60/40/20/10 acrossboth runs of all four methods) as a simple but reasonable way ofordering analytes by average importance. To break ties (same number ofTop N appearances), the mean Rank of the biomarker's appearances on theTop N lists is used. For example, as shown in Table 3, the mostimportant analyte (in the aggregate multivariate analysis), Paraoxonase1 (PON1), appears on all 32 Top N lists (it is in the Top 80, 40, 20,and 10 for each of two runs of all four methods). The second and thirdmost important analytes each appear on 30 of the 32 Top N lists. The tieis broken using Mean Rank which is 6.3 for the second most importantanalyte and 8.3 for the third most important analyte.

Example 7: Building a Prediction Model

The goal of building a predictive model is to create a model forpredicting an assessment of MS activity in a previously unseen samplefrom an individual.

Separate prediction models were built for 1) a set of biomarkers (e.g.,top 10/20/40/60/80) and 2) a method. In other words, a predictive modelwas built for the top 10 biomarkers using RF algorithms. Additionally,independent predictive models were built for the top 20, top 40, top 60,and top 80 biomarkers using RF algorithms. Independent predictive modelswere also built for the top 10/20/40/60/80 for each of GBM, LASSO, andXGB algorithms.

Specifically, as shown in Table 3, biomarkers that rank in the top 10are categorized in Set 1. Biomarkers ranked between rank 11 and rank 20,inclusive, are categorized in Set 2. Biomarkers ranked between rank 21and rank 40, inclusive, are categorized in Set 3. Biomarkers rankedbetween rank 41 and rank 60, inclusive, are categorized in Set 4.Biomarkers ranked between rank 61 and rank 80, inclusive, arecategorized in Set 5.

A subset (80%) of training samples were randomly selected, while holdingaside the remaining samples (20%) for testing and validation. Thebiomarker values were adjusted for age and sex of the individual thatthe test sample was obtained from. Additionally, biomarker values werescaled across all samples in the training set.

For each predictive model, cross validation (e.g., 10-fold, 5-repeat)was performed to select certain model parameters. Specifically, each ofthe multivariate classification methods offers parameters which can betuned in order to optimize the performance of the model produced by thatmethod. For example, RF offers the mtry parameter, which determines thenumber of variables that are randomly selected as candidates at eachnode split during the building of a tree. We use cross-validation as atechnique for selecting the best value of mtry among a set of possiblevalues. The cross-validation is performed using 10 folds of the trainingsubset which means that the training subset is randomly partitioned into10 subsamples of roughly equal size. For each of these 10 subsamples wetrain a model using 9 subsamples, and then validate the model using theone subsample that was held aside. By aggregating the results across the10 mutually exclusive subsamples, a measure of model performance for agiven value of the mtry parameter is obtained. For each candidate valuefor mtry, this validation process is repeated 5 times so as to reducethe impact of randomness involved in the subsampling step. Based on theaverage model performance of the 5 cross-validation runs, the bestperforming value of mtry is chosen, and that parameter value is used insubsequent model training using the full training subset.

Example 8: Validating a Prediction Model

Each prediction model was validated using the remaining 20% of thetraining samples. Four hundred independent iterations were conductedusing the training samples. Within one iteration, a prediction model isvalidated by making predictions for each of the samples in the testingset (in that iteration). The performance of each prediction model isreported as the area under the curve (AUC) of the testing set.

Use of statistical values such as the AUC, and specifically the AUC asit relates to a receiver operating characteristic (ROC) curve,encompassing all potential threshold or cut-off point values isgenerally used to quantify predictive model performance. Acceptabledegrees of accuracy can be defined. In certain embodiments of thepresent teachings, an acceptable degree of accuracy can be one in whichthe AUC for the ROC curve is 0.60 or higher.

In general, defining the degree of accuracy for the relevant predictivemodel or test (e.g., cut-off points on a ROC curve), defining anacceptable AUC value, and determining the acceptable ranges in relativeconcentration of what constitutes an effective amount of the biomarkersof the present teachings, allows one of skill in the art to use thebiomarkers of the present teachings to determine MS activity inindividuals with a pre-determined level of predictability andperformance.

Each prediction was a quantitative measure of the relative likelihoodthat a sample belongs to one of the two classes in question (e.g.Exacerbation vs. Quiescence). These quantitative predictions are used inthe computation of AUC, which is a measure of a model's overallperformance across the entire set of testing samples. The mean AUCacross the 400 iterations is reported for each of the methods for eachof the Top 80/40/20/10 analyte lists as shown in Table 83.

Example 9: Two Biomarker Combinations Including Paraoxonase 1 (PON1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of PON1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 4. All two biomarker combinations including PON1result in a maximum AUC above the threshold value, indicating that atleast one of the predictive models is predictive of a state of MS in anindividual when considering two biomarker combinations, wherein one ofthe two biomarkers is PON1.

Example 10: Two Biomarker Combinations Including Myoglobin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Myoglobin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 5. All two biomarker combinations including Myoglobinresult in a maximum AUC above the threshold value, indicating that atleast one of the predictive models is predictive of a state of MS in anindividual when considering two biomarker combinations, wherein one ofthe two biomarkers is Myoglobin.

Example 11: Two Biomarker Combinations Including Plasminogen ActivatorInhibitor 1 (PAI-1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of PAI-1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 6. All two biomarker combinations including PAI-1result in a maximum AUC above the threshold value, indicating that atleast one of the predictive models is predictive of a state of MS in anindividual when considering two biomarker combinations, wherein one ofthe two biomarkers is PAI-1.

Example 12: Two Biomarker Combinations Including Tissue Inhibitor ofMetalloproteinases 1 (TIMP1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of TIMP1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 7. A majority of biomarker combinations includingTIMP1 result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is TIMP1.

Example 13: Two Biomarker Combinations Including Stromal Cell DerivedFactor 1 (SDF1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of SDF1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 8. A majority of biomarker combinations including SDF1result in a maximum AUC above the threshold value, indicating that atleast one of the predictive models is predictive of a state of MS in anindividual when considering two biomarker combinations, wherein one ofthe two biomarkers is SDF1.

Example 14: Two Biomarker Combinations Including Interleukin 6 ReceptorSubunit Beta (IL6Rbeta)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IL6Rbeta and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 9. All two biomarker combinations including IL6Rbetaresult in a maximum AUC above the threshold value, indicating that atleast one of the predictive models is predictive of a state of MS in anindividual when considering two biomarker combinations, wherein one ofthe two biomarkers is IL6Rbeta.

Example 15: Two Biomarker Combinations Including Cystatin B

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Cystatin B and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 10. All two biomarker combinations including CystatinB result in a maximum AUC above the threshold value, indicating that atleast one of the predictive models is predictive of a state of MS in anindividual when considering two biomarker combinations, wherein one ofthe two biomarkers is Cystatin B.

Example 16: Two Biomarker Combinations Including Immunoglobulin E (IgE)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IgE and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 11. All two biomarker combinations including IgE result in amaximum AUC above the threshold value, indicating that at least one ofthe predictive models is predictive of a state of MS in an individualwhen considering two biomarker combinations, wherein one of the twobiomarkers is IgE.

Example 17: Two Biomarker Combinations Including Macrophage InflammatoryProtein 3 Beta (MIP3beta)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of MIP3beta and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 12. A majority of biomarker combinations includingMIP3beta result in a maximum AUC above the threshold value, indicatingthat at least one of the predictive models is predictive of a state ofMS in an individual when considering two biomarker combinations, whereinone of the two biomarkers is MIP3beta.

Example 18: Two Biomarker Combinations Including Vascular Cell AdhesionMolecule 1 (VCAM1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of VCAM1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 13. All biomarker combinations including VCAM1 resultin a maximum AUC above the threshold value, indicating that at least oneof the predictive models is predictive of a state of MS in an individualwhen considering two biomarker combinations, wherein one of the twobiomarkers is VCAM1.

Example 19: Two Biomarker Combinations Including Macrophage DerivedChemokine (MDC)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of MDC and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 14. All biomarker combinations including MDC result in amaximum AUC above the threshold value, indicating that at least one ofthe predictive models is predictive of a state of MS in an individualwhen considering two biomarker combinations, wherein one of the twobiomarkers is MDC.

Example 20: Two Biomarker Combinations Including Vascular EndothelialGrowth Factor (VEGF)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of VEGF and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 15. A majority of biomarker combinations includingVEGF result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is VEGF.

Example 21: Two Biomarker Combinations Including Ficolin 3

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Ficolin 3 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 16. All biomarker combinations including Ficolin 3result in a maximum AUC above the threshold value, indicating that atleast one of the predictive models is predictive of a state of MS in anindividual when considering two biomarker combinations, wherein one ofthe two biomarkers is Ficolin 3.

Example 22: Two Biomarker Combinations Including Immunoglobulin a (ISA)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IgA and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 17. A majority of biomarker combinations including IgA resultin a maximum AUC above the threshold value, indicating that at least oneof the predictive models is predictive of a state of MS in an individualwhen considering two biomarker combinations, wherein one of the twobiomarkers is IgA.

Example 23: Two Biomarker Combinations Including Factor VII

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Factor VII and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 18. A majority of biomarker combinations includingFactor VII result in a maximum AUC above the threshold value, indicatingthat at least one of the predictive models is predictive of a state ofMS in an individual when considering two biomarker combinations, whereinone of the two biomarkers is Factor VII.

Example 24: Two Biomarker Combinations Including Interleukin 6 Receptor(IL6R)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IL6R and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 19. A majority of biomarker combinations includingIL6R result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is IL6R.

Example 25: Two Biomarker Combinations Including Receptor for AdvancedGlycosylation End Products (RAGE)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of RAGE and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 20. A majority of biomarker combinations includingRAGE result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is RAGE.

Example 26: Two Biomarker Combinations Including Fibulin 1C (FIB1C)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of FIB1C and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 21. A significant number of biomarker combinationsincluding FIB1C result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is FIB1C.

Example 27: Two Biomarker Combinations Including Interferon Inducble TCell Alpha Chemoattractant (ITAC)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of ITAC and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 22. A significant number of biomarker combinationsincluding ITAC result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is ITAC.

Example 28: Two Biomarker Combinations Including Growth Hormone (GH)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of GH and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 23. A majority of biomarker combinations including GH result ina maximum AUC above the threshold value, indicating that at least one ofthe predictive models is predictive of a state of MS in an individualwhen considering two biomarker combinations, wherein one of the twobiomarkers is GH.

Example 29: Two Biomarker Combinations Including Heparin Binding EGFLike Growth Factor (HBEGF)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of HBEGF and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 24. A majority of biomarker combinations includingHBEGF result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is HBEGF.

Example 30: Two Biomarker Combinations Including Neuronal Cell AdhesionMolecule (NrCAM)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of NrCAM and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 25. A majority of biomarker combinations includingNrCAM result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is NrCAM.

Example 31: Two Biomarker Combinations Including Growth Regulated AlphaProtein (GROalpha)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of GROalpha and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 26. A significant number of biomarker combinationsincluding GROalpha result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is GROalpha.

Example 32: Two Biomarker Combinations Including Growth DifferentiationFactor 15 (GDF15)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of GDF15 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 27. A significant number of biomarker combinationsincluding GDF15 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is GDF15.

Example 33: Two Biomarker Combinations Including Mast Stem Cell GrowthFactor Receptor (SCFR)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of SCFR and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 28. A significant number of biomarker combinationsincluding SCFR result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is SCFR.

Example 34: Two Biomarker Combinations Including Cadherin 1 (Ecad)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Ecad and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 29. A significant number of biomarker combinationsincluding Ecad result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Ecad.

Example 35: Two Biomarker Combinations Including Angiogenin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Angiogenin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 30. A significant number of biomarker combinationsincluding Angiogenin result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Angiogenin.

Example 36: Two Biomarker Combinations Including Sortilin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Sortilin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 31. A significant number of biomarker combinationsincluding Sortilin result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Sortilin.

Example 37: Two Biomarker Combinations Including Alpha 1 Antitrypsin(AAT)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of AAT and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 32. A significant number of biomarker combinations includingAAT result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is AAT.

Example 38: Two Biomarker Combinations Including Immunoglobulin M (IgM)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IgM and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 33. A significant number of biomarker combinations includingIgM result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is IgM.

Example 39: Two Biomarker Combinations Including Pulmonary andActivation Regulated Chemokine (PARC)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of PARC and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 34. A significant number of biomarker combinationsincluding PARC result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is PARC.

Example 40: Two Biomarker Combinations Including Pulmonary SurfactantAssociated Protein D (SP-D)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of SP-D and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 35. A significant number of biomarker combinationsincluding SP-D result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is SP-D.

Example 41: Two Biomarker Combinations Including B Cell ActivatingFactor (BAFF)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of BAFF and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 36. A significant number of biomarker combinationsincluding BAFF result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is BAFF.

Example 42: Two Biomarker Combinations Including Adrenomedullin (ADM)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of ADM and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 37. A majority of biomarker combinations including ADM resultin a maximum AUC above the threshold value, indicating that at least oneof the predictive models is predictive of a state of MS in an individualwhen considering two biomarker combinations, wherein one of the twobiomarkers is ADM.

Example 43: Two Biomarker Combinations Including Pigment EpitheliumDerived Factor (PEDF)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of PEDF and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 38. A majority of biomarker combinations includingPEDF result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is PEDF.

Example 44: Two Biomarker Combinations Including Interleukin 1 ReceptorAntagonist (IL1ra)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IL1ra and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 39. A significant number of biomarker combinationsincluding IL1ra result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is IL1ra.

Example 45: Two Biomarker Combinations Including Thyroxine BindingGlobulin (TBG)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of TBG and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 40. A significant number of biomarker combinations includingTBG result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is TBG.

Example 46: Two Biomarker Combinations Including Microalbumin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Microalbumin and one other biomarker from Table 3 wasassessed. Summary statistics, including a maximum AUC across the twomethods, are depicted in Table 41. A significant number of biomarkercombinations including Microalbumin result in a maximum AUC above thethreshold value, indicating that at least one of the predictive modelsis predictive of a state of MS in an individual when considering twobiomarker combinations, wherein one of the two biomarkers isMicroalbumin.

Example 47: Two Biomarker Combinations Including Leptin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Leptin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 42. A majority of biomarker combinations includingLeptin result in a maximum AUC above the threshold value, indicatingthat at least one of the predictive models is predictive of a state ofMS in an individual when considering two biomarker combinations, whereinone of the two biomarkers is Leptin.

Example 48: Two Biomarker Combinations Including Eotaxin 2

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Eotaxin 2 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 43. A significant number of biomarker combinationsincluding Eotaxin 2 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Eotaxin 2.

Example 49: Two Biomarker Combinations Including Insulin Like GrowthFactor Binding Protein 2 (IGFBP2)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IGFBP2 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 44. A significant number of biomarker combinationsincluding IGFBP2 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is IGFBP2.

Example 50: Two Biomarker Combinations Including Resistin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Resistin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 45. A significant number of biomarker combinationsincluding Resistin result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Resistin.

Example 51: Two Biomarker Combinations Including Cathepsin D

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Cathepsin D and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 46. A majority of biomarker combinations includingCathepsin D result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Cathepsin D.

Example 52: Two Biomarker Combinations Including E-Selectin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of E-Selectin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 47. A majority of biomarker combinations includingE-Selectin result in a maximum AUC above the threshold value, indicatingthat at least one of the predictive models is predictive of a state ofMS in an individual when considering two biomarker combinations, whereinone of the two biomarkers is E-Selectin.

Example 53: Two Biomarker Combinations Including YKL40

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of YKL40 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 48. A significant number of biomarker combinationsincluding YKL40 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is YKL40.

Example 54: Two Biomarker Combinations Including Interleukin 22 (IL22)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IL22 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 49. A significant number of biomarker combinationsincluding IL22 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is IL22.

Example 55: Two Biomarker Combinations Including CarcinoembryonicAntigen (CEA)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of CEA and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 50. A significant number of biomarker combinations includingCEA result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is CEA.

Example 56: Two Biomarker Combinations Including Interleukin 8 (IL8)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IL8 and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 51. A significant number of biomarker combinations includingIL8 result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is IL8.

Example 57: Two Biomarker Combinations Including Cancer Antigen 15-3 (CA15-3)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of CA 15-3 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 52. A significant number of biomarker combinationsincluding CA 15-3 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is CA 15-3.

Example 58: Two Biomarker Combinations Including Leptin Receptor(LeptinR)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of LeptinR and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 53. A significant number of biomarker combinationsincluding LeptinR result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is LeptinR.

Example 59: Two Biomarker Combinations Including Insulin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Insulin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 54. A significant number of biomarker combinationsincluding Insulin result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Insulin.

Example 60: Two Biomarker Combinations Including Monocyte ChemotacticProtein 1 (MCP1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of MCP1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 55. A significant number of biomarker combinationsincluding MCP1 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is MCP1.

Example 61: Two Biomarker Combinations Including Prolactin (PRL)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of PRL and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 56. A significant number of biomarker combinations includingPRL result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is PRL.

Example 62: Two Biomarker Combinations Including Tetranectin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Tetranectin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 57. A significant number of biomarker combinationsincluding Tetranectin result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Tetranectin.

Example 63: Two Biomarker Combinations Including CarcinoembryonicAntigen Related Cell Adhesion Molecule 1 (CEACAM1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of CEACAM1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 58. A significant number of biomarker combinationsincluding CEACAM1 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is CEACAM1.

Example 64: Two Biomarker Combinations Including 6Ckine

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of 6Ckine and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 59. A significant number of biomarker combinationsincluding 6Ckine result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is 6Ckine.

Example 65: Two Biomarker Combinations Including Serum Amyloid PComponent (SAP)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of SAP and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 60. A significant number of biomarker combinations includingSAP result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is SAP.

Example 66: Two Biomarker Combinations Including Complement Factor HRelated Protein 1 (CFHR1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of CFHR1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 61. A significant number of biomarker combinationsincluding CFHR1 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is CFHR1.

Example 67: Two Biomarker Combinations Including Chemokine CC-4 (HCC-4)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of HCC-4 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 62. A significant number of biomarker combinationsincluding HCC-4 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is HCC-4.

Example 68: Two Biomarker Combinations Including Complement C3 (C3)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of C3 and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 63. A significant number of biomarker combinations including C3result in a maximum AUC above the threshold value, indicating that atleast one of the predictive models is predictive of a state of MS in anindividual when considering two biomarker combinations, wherein one ofthe two biomarkers is C3.

Example 69: Two Biomarker Combinations Including Alpha Fetoprotein (AFP)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of AFP and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 64. A significant number of biomarker combinations includingAFP result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is AFP.

Example 70: Two Biomarker Combinations Including Angiopoietin 1 (ANG-1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of ANG-1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 65. A significant number of biomarker combinationsincluding ANG-1 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is ANG-1.

Example 71: Two Biomarker Combinations Including Interleukin 18 (IL18)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IL18 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 66. A significant number of biomarker combinationsincluding IL18 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is IL18.

Example 72: Two Biomarker Combinations Including Gelsolin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Gelsolin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 67. A significant number of biomarker combinationsincluding Gelsolin result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Gelsolin.

Example 73: Two Biomarker Combinations Including Tenascin C (TN-C)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of TN-C and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 68. A significant number of biomarker combinationsincluding TN-C result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is TN-C.

Example 74: Two Biomarker Combinations Including Vitronectin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Vitronectin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 69. A significant number of biomarker combinationsincluding Vitronectin result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Vitronectin.

Example 75: Two Biomarker Combinations Including Beta 2 Microglobulin(B2M)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of B2M and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 70. A significant number of biomarker combinations includingB2M result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is B2M.

Example 76: Two Biomarker Combinations Including Pancreatic SecretoryTrypsin Inhibitor (TATI)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of TATI and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 71. A significant number of biomarker combinationsincluding TATI result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is TATI.

Example 77: Two Biomarker Combinations Including MatrixMetalloproteinase 3 (MMP3)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of MMP3 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 72. A significant number of biomarker combinationsincluding MMP3 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is MMP3.

Example 78: Two Biomarker Combinations Including Omentin

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Omentin and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 73. A significant number of biomarker combinationsincluding Omentin result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Omentin.

Example 79: Two Biomarker Combinations Including Interleukin 18 BindingProtein (IL 18bp)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of IL 18bp and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 74. A significant number of biomarker combinationsincluding IL 18bp result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is IL 18bp.

Example 80: Two Biomarker Combinations Including Apolipoprotein D (ApoD)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of ApoD and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 75. A significant number of biomarker combinationsincluding ApoD result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is ApoD.

Example 81: Two Biomarker Combinations Including Monocyte ChemotacticProtein 4 (MCP-4)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of MCP-4 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 76. A significant number of biomarker combinationsincluding MCP-4 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is MCP-4.

Example 82: Two Biomarker Combinations Including Apolipoprotein E(Apo-E)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Apo-E and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 77. A significant number of biomarker combinationsincluding Apo-E result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is Apo-E.

Example 83: Two Biomarker Combinations Including ST2

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of ST2 and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 78. A significant number of biomarker combinations includingST2 result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is ST2.

Example 84: Two Biomarker Combinations Including Thrombospondin 1

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of Thrombospondin 1 and one other biomarker from Table 3 wasassessed. Summary statistics, including a maximum AUC across the twomethods, are depicted in Table 79. A significant number of biomarkercombinations including Thrombospondin 1 result in a maximum AUC abovethe threshold value, indicating that at least one of the predictivemodels is predictive of a state of MS in an individual when consideringtwo biomarker combinations, wherein one of the two biomarkers isThrombospondin 1.

Example 85: Two Biomarker Combinations Including Gastric InhibitoryPolypeptide (GIP)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of GIP and one other biomarker from Table 3 was assessed. Summarystatistics, including a maximum AUC across the two methods, are depictedin Table 80. A significant number of biomarker combinations includingGIP result in a maximum AUC above the threshold value, indicating thatat least one of the predictive models is predictive of a state of MS inan individual when considering two biomarker combinations, wherein oneof the two biomarkers is GIP.

Example 86: Two Biomarker Combinations Including MatrixMetalloproteinase 7 (MMP7)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of MMP7 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 81. A significant number of biomarker combinationsincluding MMP7 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is MMP7.

Example 87: Two Biomarker Combinations Including Intercellular AdhesionMolecule 1 (ICAM-1)

The ability of the predictive models to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of ICAM-1 and one other biomarker from Table 3 was assessed.Summary statistics, including a maximum AUC across the two methods, aredepicted in Table 82. A significant number of biomarker combinationsincluding ICAM-1 result in a maximum AUC above the threshold value,indicating that at least one of the predictive models is predictive of astate of MS in an individual when considering two biomarkercombinations, wherein one of the two biomarkers is ICAM-1.

Example 88: Additional Biomarker Combinations

The ability of the predictive model to determine the state (e.g.,quiescent or exacerbation) of MS in an individual using expressionvalues of additional biomarker combinations from Table 3 was assessed.Specifically, biomarker combinations were as follows: 1) 10 biomarkersfrom set 1, 2) 10 biomarkers from set 1 and 10 biomarkers from set 2, 3)10 biomarkers from set 1, 10 biomarkers from set 2, and 20 biomarkersfrom set 3, 4) 10 biomarkers from set 1, 10 biomarkers from set 2, 20biomarkers from set 3, and 20 biomarkers from set 4, and 5) 10biomarkers from set 1, 10 biomarkers from set 2, 20 biomarkers from set3, 20 biomarkers from set 4, and 20 biomarkers from set 5.

Three methods were used for the assessment of the predictive model:random forest, gradient boosting, and LASSO. For all analyses, the areaunder the curve (AUC) was the primary accuracy metric used. For eachanalysis, the mean AUC is depicted in Table 83. AUCs greater than athreshold value of 0.6 are considered predictive. As shown in Table 83,each combination of biomarkers (e.g., N=10, 20, 40, 60, or 80 totalbiomarkers) yields an AUC above the threshold value, indicating that thepredictive model is predictive of a state of MS in an individual.

In particular, when the predictive model was assessed using a randomforest method, the highest mean AUC (mean AUC=0.857) was observed forN=20 total biomarkers (biomarkers from set 1 and set 2). When thepredictive model was assessed using a gradient boosting method, thehighest mean AUC (mean AUC=0.873) was observed for N=40 total biomarkers(biomarkers from set 1, set 2, and set 3). When the predictive model wasassessed using a LASSO method, the highest mean AUC (mean AUC=0.829) wasobserved for N=60 total biomarkers (biomarkers from set 1, set 2, set 3,and set 4).

Methods

General Study Design and Study Population

Test samples were obtained from subjects as a part of the AcceleratedCure Project (ACP), which includes 10 leading MS clinics across theUnited States. The study was approved at the institutional review boardat all participating centers and all patients gave written informedconsent. Eligible subjects included in the ACP study includedindividuals with at least one central nervous system demyelinating eventcharacteristic of MS, transverse myelitis (TM), acute disseminatedencephalomyelitis (ADEM), neuromyelitis optica (NMO), and optic neuritis(ON). Subjects were ineligible if the individual presented with clinicalor radiological evidence of stroke, meningitis, neoplastic, peripheralnervous system or primary muscle disease, or other well characterizedand defined diseases of the nervous system with the exception of MS, TM,ADEM, NMO, ON. Ineligible subjects also included individuals with ahistory of blood borne pathogens, history of allogeneic bone marrowtransplant, and individuals who weigh less than 37 pounds due to limitson blood collection.

Blood Collection

Blood serum samples were collected accorded to sample processinginstructions provided by the Accelerated Cure Project. Specifically,blood (up to 110 mL) was drawn into tiger top SST tubes, inverted 5times, and left to sit in an upright position for 30-60 minutes to allowclotting. Tubes were centrifuged at 3,000 RPM (approximately 1000×g) for10 minutes. Serum was then transferred in 0.5 mL aliquots into 1.0 mLcryovials using a plastic pipette. Cryovials were stored frozen at thecollection site at −70 to −80° C. until shipment to SeraCare. Sampleswere batched at least monthly and shipped frozen on dry ice usingovernight delivery. Cryovials were stored frozen at SeraCare at −80° C.

Marker Quantification

Multiplex analysis was performed using multiplex luminex analysis(Rules-Based Medicine (Austin, Tex.)), which uses Multi-Analyte Profiles(MAPs) based on powerful Luminex xMAP® technology to discover biomarkerexpression values within very small sample volumes.

Statistical Modeling

Logistic Regression

Logistic Regression is the traditional predictive modeling method ofchoice for dichotomous response variables; e.g., positive diagnosis vsnegative diagnosis. It can be used to model both linear and non-linearaspects of the data variables. A series of logistic regression modelswere fit to the quantitative expression values of one or morebiomarkers. Specifically, logistic regression models were generated forone biomarker (univariate), two biomarkers, or the top 10, top 20, top40, top 60, or top 80 biomarkers as described above.

Random Forest (RF)

Random Forest models are based upon the idea of creating hundreds ofregression trees as models. Each regression tree model is created with auniform number of terminal nodes (“leaves”) at the end of the branchesof the tree. To estimate the regression value of a new individual, or toassign the individual to a class, quantitative expression levels ofbiomarkers from a test sample obtained from the individual is evaluatedwithin each of the regression tree models. The output prediction (i.e.,regression value if continuous data, classification if binary data) fromall trees is then averaged to create the final regression value or classprediction. In the case of regression values, averaging may be obtainedby a weighted average; in class prediction, simply by voting.

The Random Forest methodology was as follows. First, a bootstrap sample(i.e., a sample with replacement) was drawn from the original data. Thena regression tree was “grown” from each bootstrap sample; i.e., at eachnode one randomly samples p of the n biomarkers measured, and selectsthe best biomarker and the best value of that biomarker to split thedata into pure subsets from those biomarkers. Data from “training”subjects were used to build the tree models. Then, new data waspredicted by aggregating the predictions of the various regression treesthus derived. Then, new data was predicted by aggregating thepredictions of the various regression trees thus derived. For eachsubject sample k, where the k subject samples were different from thoseused in training the model (i.e., all k samples are “out of the bag”),the response estimates was averaged over the trees, given as ŷ_(k). Therandom forest prediction algorithm was then given by the equation:

f = E XY  ( Y - h _  ( X ) ) 2 = 1 k  ∑ ( y k - y ^ k ) 2

where

_(f) was a test set estimate of the generalization error of PE_(f), and

${\overset{\_}{h}(X)} = {\left( \frac{1}{L} \right){\sum{h\left( {x;\theta_{l}} \right)}}}$

was the random forest prediction. The collection of tree predictors wasgiven by h(x; θ_(l)), l=1, . . . , L, where θ_(l) is a random vector. Yrepresented the actual ground truth (e.g., the indication).

The variable importance was then estimated. In every regression treethus grown in the random forest, one calculated the prediction error forthat tree,

${{PE}_{t} = {\frac{1}{K}{\sum\left( {y_{k} - {\hat{y}}_{k}} \right)^{2}}}},$

as predicted by the lth tree predictor, h(x; θ_(l)). One then randomlypermuted the values of a biomarker variable i in the “out of bag” cases,and computed the prediction error as predicted by the lth treepredictor. Importance (Imp) was given as the variable i forImp_(i)=PE_(l)i−PE_(l) for the ith biomarker for lth tree. The variableimportance of the ith variable was computed I_(i)=

$\frac{I\overset{\_}{m}p_{i}}{{SE}\left( {Imp}_{i} \right)}$

where Imp_(i) was the average and standard area of importance of ithvariable over all L trees.

Gradient Boosting

Boosted Trees models are based upon the idea of computing a sequence oftrees, where each successive tree is built by predicting the residualsof the preceding tree. Put another way, boosting will generate asequence of classifiers, where each consecutive classifier in thesequence is an “expert” in classifying observations that were not wellclassified by those preceding it. Gradient boosting is described infurther detail by Friedman, Jerome H. “Greedy function approximation: agradient boosting machine.” Annals of statistics (2001): 1189-1232,which is hereby incorporated by reference in its entirety.

The variable importance input variables for a GBM model is as follows.For an output {circumflex over (F)}(x), the relative influence I_(j) orinput variable x_(j) is estimated as:

Î _(j) ²(T)=Σ_(t=1) ^(J−1) i _(t) ²1(v _(t) =j)  (1)

where the summation is over the non-terminal nodes t of the J-terminalnode tree T, v_(t) is the splitting variable associated with node t, andî_(t) ² is the corresponding empirical improvement in squared-error as aresult of the split. For a collection of decision trees {T_(m)}₁ ^(M)obtained through boosting, Equation 1 above can be generalized by itsoverage over all of the trees in the sequence as:

$\begin{matrix}{{\hat{I}}_{j}^{2} = {\frac{1}{M}{\sum_{m = 1}^{M}{{\hat{I}}_{j}^{2}\left( T_{m} \right)}}}} & (2)\end{matrix}$

The importance of each input variable (e.g., biomarker) can be rankedaccording to their respective relative influence I_(j).

Extreme Gradient Boosting (XGB)

XGB is an independent implementation of boosted trees and is hencesimilar in approach to the GBM method. XGB uses additionalregularization within its model in order to limit overfitting of thedata (regularization is a way of penalizing model complexity, so as toavoid highly complex models that fit the training data well but don'tgeneralize well to new data). XGB is described in further detail by Chenet al (Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable TreeBoosting System. In Proceedings of the 22nd ACM SIGKDD InternationalConference on Knowledge Discovery and Data Mining (KDD '16). ACM, NewYork, N.Y., USA, 785-794), which is hereby incorporated by reference inits entirety.

The variable importance of input variables for a XGB model is similar tothe GBM model. Specifically, the input variables can be ranked accordingto a “gain” where gain is the improvement in accuracy brought by aninput feature to the branches it is on. The idea is that before adding anew split on a feature X to the branch, there were some wronglyclassified elements. After adding the split on this feature, there aretwo new branches, and each of these branches is more accurate (onebranch saying if your observation is on this branch then it should beclassified as 1, and the other branch saying the exact opposite).

Least Absolute Shrinkage and Selection Operator (LASSO)

Penalized regression model methods are a set of statistical techniquesthat select subsets of variables to include in a model and determinestable coefficients for the variables. These methods are particularlyuseful when variables are correlated, and include ridge regression,Lasso, Elastic Net, and other methods. All of these methods have thecharacteristic that they shrink (penalize) the coefficients in theregression model.

Least Absolute Shrinkage and Selection Operator (LASSO or Lasso) wasused to prioritize biomarkers (based on R2 values) and to obtain a Lassomodel. The “lasso” in this model minimized the residual sum of thesquare, subject to the sum of the absolute value of the coefficientsbeing less than a constant. See R. Tibshirani, J. Royal Stat. Soc.,series B 1996, 58(1):267-288. The Lasso method produced interpretablemodels, such as subset selection, and exhibited the stability of ridgeregression (a statistical method that shrinks and stabilizescoefficients in regression models with multicollinearity). See W.Mendenhall and T. Sincich, A Second Course in Statistics: RegressionAnalysis, 6th edition 2003, Pearson Prentice Hall, Inc., Upper SaddleRiver, N.J.

Discussion of Methods

Many of these techniques are useful either combined with a biomarkerselection technique (such as, for example, forward selection, backwardsselection, or stepwise selection), or for complete enumeration of allpotential panels of a given size, or genetic algorithms, or they canthemselves include biomarker selection methodologies in their owntechniques. These techniques can be coupled with information criteria,such as Akaike's Information Criterion (AIC), Bayes InformationCriterion (BIC), or cross-validation, to quantify the tradeoff betweenthe inclusion of additional biomarkers and model improvement, and tominimize overfit. The resulting predictive models can be validated inother studies, or cross-validated in the study they were originallytrained in, using such techniques as, for example, Leave-One-Out (LOO)and 10-Fold cross-validation.

Additional Embodiments

While the invention has been particularly shown and described withreference to a preferred embodiment and various alternate embodiments,it will be understood by persons skilled in the relevant art thatvarious changes in form and details can be made therein withoutdeparting from the spirit and scope of the invention.

All references, issued patents and patent applications cited within thebody of the instant specification are hereby incorporated by referencein their entirety, for all purposes.

TABLE 1 Phase 1 Cohort Phase I-Accelerated Cure Project (ACP) CohortTotal Samples (N) 125 Female 94 Male 31 Age range 20-60 years old Yearssince diagnosis 0-10 Current State Exacerbation 60 Quiescent 65

TABLE 2 Univariate Analysis of Identified Biomarker Candidates AnalyteP-value AUC 1 6Ckine 0.052563 0.607949 2 Adiponectin 0.781103 0.565641 3Adrenomedullin (ADM) 0.031053 0.621538 4 Alpha-1 Antitrypsin (AAT)0.142885 0.546667 5 Alpha-1-Microglobulin (A1Micro) 0.50261 0.528718 6Alpha-2-Macroglobulin (A2Macro) 0.396704 0.503333 7 Alpha-Fetoprotein(AFP) 0.095375 0.546923 8 Amphiregulin (AR) 0.795756 0.495256 9Angiogenin 0.319132 0.582308 10 Angiopoietin 1 (ANG-1) 0.601165 0.53910311 Angiopoietin 2 (ANG-2) 0.132375 0.559615 12 Angiotensin ConvertingEnzyme (ACE) 0.355858 0.555128 13 Antileukoproteinase (ALP) 0.2528670.547821 14 Antithrombin III (ATIII) 0.487011 0.543077 15 ApolipoproteinA (Apo-A) 0.179049 0.482308 16 Apolipoprotein D (Apo-D) 0.5147270.551026 17 Apolipoprotein E (Apo-E) 0.025992 0.599744 18 AXL ReceptorTyrosine Kinase (AXL) 0.615571 0.500128 19 B-cell activating factor(BAFF) 0.776797 0.492436 20 B Lymphocyte Chemoattractant (BLC) 0.4902960.490513 21 Beta-Amyloid (1-40) (AB-40) 0.24407 0.569744 22 Beta-Amyloid(1-42) (AB-42) 0.457162 0.547564 23 Beta-2 Microglobulin (B2M) 0.2496750.560256 24 Betacellulin (BTC) NA NA 25 Brain Derived NeurotrophicFactor (BDNF) 0.800508 0.533462 26 C-Reactive Protein (CRP) 0.1171690.548846 27 Cadherin 1 (E-Cad) 0.728647 0.550513 28 Calbindin 0.9781260.508205 29 Cancer Antigen 125 (CA-125) 0.940383 0.479103 30 CancerAntigen 15-3 (CA 15-3) 0.452502 0.54141 31 Cancer Antigen 19-9 (CA 19-9)0.656405 0.503333 32 Carbonic anhydrase 9 (CA-9) 0.240729 0.542436 33Carcinoembryonic Antigen (CEA) 0.307703 0.563205 34 Carcinoembryonicantigen related cell adhesion molecule 1 0.183887 0.564872 (CEACAM1) 35Cathepsin D 0.027467 0.620513 36 CD40 Ligand (CD40-L) 0.686825 0.51769237 CD163 0.299555 0.546154 38 Ceruloplasmin 0.785238 0.54359 39Chemokine CC-4 (HCC-4) 0.723205 0.495256 40 Chromogranin A (CgA)0.986027 0.537436 41 Ciliary Neurotrophic Factor (CNTF) 0.1468770.553718 42 Clusterin (CLU) 0.864157 0.491154 43 Complement C3 (C3)0.399032 0.513205 44 Complement Factor H (CFH) 0.043004 0.581282 45Complement Factor H Related Protein 1 (CFHR1) 0.956458 0.502949 46Cystatin B 0.029893 0.641282 47 CystatinC 0.135032 0.572051 48 Decorin0.416237 0.547692 49 Dickkopf related protein 1 (DKK-1) 0.4290320.488333 50 Dopamine beta hydroxylase (DBH) 0.800301 0.527821 51E-Selectin 0.010997 0.632179 52 EN-RAGE 0.671174 0.54141 53 Eotaxin-10.957069 0.501923 54 Eotaxin-2 0.498705 0.572308 55 Eotaxin-3 0.3417680.509487 56 Epidermal Growth Factor (EGF) 0.923961 0.514615 57 EpidermalGrowth Factor Receptor (EGFR) 0.974963 0.503846 58 Epiregulin (EPR)0.871179 0.475641 59 Epithelial Derived Neutrophil Activating Protein 78(ENA-78) 0.817716 0.520769 60 Erythropoietin (EPO) 0.892677 0.453974 61Factor VII 0.062994 0.578718 62 Fas Ligand (FasL) 0.181567 0.545385 63FASLG Receptor (FAS) 0.737959 0.502051 64 Ferritin (FRTN) 0.748020.535128 65 Fibrinogen 0.581972 0.488077 66 Fibulin 1C (Fib1C) 0.4174860.577821 67 Ficolin 3 0.050576 0.613333 68 Follicle Stimulating Hormone(FSH) 0.928345 0.529744 69 Gastric inhibitory polypeptide (GIP) 0.1257130.588077 70 Gelsolin 0.438391 0.466923 71 Glucagon Like Peptide-1(GLP-1) 0.863589 0.512051 72 Glycogen phosphorylase isoenzyme BB (GPBB)0.243271 0.517051 73 Granulocyte Colony Stimulating Factor (GCSF)0.415708 0.507051 74 Granulocyte Macrophage Colony Stimulating Factor(GM-CSF) NA NA 75 Growth differentiation factor 15 (GDF-15) 0.4119470.459872 76 Growth Hormone (GH) 0.326537 0.605769 77 Growth Regulatedalpha protein (GROalpha) 0.499583 0.535769 78 Haptoglobin 0.1304740.581538 79 Heat Shock protein 70 (HSP-70) 0.933051 0.482308 80 HeparinBinding EGF Like Growth Factor (HB-EGF) 0.350227 0.529487 81 HepatocyteGrowth Factor (HGF) 0.612673 0.493846 82 Human Chorionic Gonadotropinbeta (hCG) 0.303706 0.528077 83 Immunoglobulin A (IgA) 0.05694 0.58384684 Immunoglobulin E (IgE) 0.121457 0.582692 85 Immunoglobulin M (IgM)0.509751 0.541538 86 Insulin 0.074983 0.607949 87 Insulin like GrowthFactor Binding Protein 2 (IGFBP2) 0.232107 0.565897 88 IntercellularAdhesion Molecule 1 (ICAM-1) 0.539951 0.494103 89 Interferon alpha(IFN-alpha) NA NA 90 Interferon gamma (IFN-gamma) NA NA 91 Interferongamma Induced Protein 10 (IP-10) 0.351582 0.521667 92 Interferoninducible T cell alpha chemoattractant (ITAC) 0.578611 0.562051 93Interleukin 1 alpha (IL-1alpha) NA NA 94 Interleukin 1 beta (IL-1beta)NA NA 95 Interleukin 1 receptor antagonist (IL1ra) 0.011251 0.584103 96Interleukin 2 (IL-2) NA NA 97 Interleukin 2 receptor alpha(IL2receptoralpha) 0.366795 0.562051 98 Interleukin 3 (IL-3) 0.2300160.543718 99 Interleukin 4 (IL-4) NA NA 100 Interleukin 5 (IL-5) NA NA101 Interleukin 6 (IL-6) 0.351258 0.497692 102 Interleukin 6 receptor(IL6r) 0.117127 0.580256 103 Interleukin 6 receptor subunit beta(IL6Rbeta) 0.00971 0.630385 104 Interleukin 7 (IL-7) 0.335385 0.501538105 Interleukin 8 (IL-8) 0.966306 0.543846 106 Interleukin 10 (IL-10)0.076322 0.572308 107 Interleukin 12 Subunit p40 (IL12p40) 0.8741750.522179 108 Interleukin 12 Subunit p70 (IL12p70) NA NA 109 Interleukin13 (IL13) 0.417776 0.490513 110 Interleukin 15 (IL15) 0.108857 0.563333111 Interleukin 16 (IL16) 0.855117 0.518974 112 Interleukin 17 (IL17) NANA 113 Interleukin 18 (IL18) 0.013593 0.627564 114 Interleukin 18binding protein (IL18bp) 0.09585 0.580513 115 Interleukin 22 (IL22)0.232761 0.569615 116 Interleukin 23 (IL23) NA NA 117 Interleukin 31(IL31) 0.28503 0.534103 118 Kidney Injury Molecule 1 (KIM-1) 0.1948140.482179 119 Lactoferrin (LTF) 0.28361 0.552308 120 Latency AssociatedPeptide of Transforming Growth Factor 0.691925 0.516795 beta 1 (LAP TGFb1) 121 Leptin 0.03778 0.598846 122 Leptin Receptor (Leptin R) 0.2464610.586282 123 Leucine rich alpha 2 glycoprotein (LRG1) 0.537216 0.505385124 Luteinizing Hormone (LH) 0.299057 0.532179 125 Macrophage ColonyStimulating Factor 1 (M-CSF) 0.701042 0.491538 126 Macrophage DerivedChemokine (MDC) 0.016395 0.625897 127 Macrophage Inflammatory Protein 1alpha (MIP1-alpha) 0.030048 0.564744 128 Macrophage Inflammatory Protein1 beta (MIP1-beta) 0.980452 0.502436 129 Macrophage Inflammatory Protein3 alpha (MIP3-alpha) 0.754238 0.550513 130 Macrophage InflammatoryProtein 3 beta (MIP3-beta) 0.044677 0.620769 131 Macrophage MigrationInhibitory Factor (MIF) 0.318299 0.470897 132 Macrophage StimulatingProtein (MSP) 0.217264 0.514487 133 Mast stem cell growth factorreceptor (SCFR) 0.070457 0.595513 134 Matrix Metalloproteinase 1 (MMP-1)0.785116 0.550513 135 Matrix Metalloproteinase 2 (MMP-2) 0.2717190.535385 136 Matrix Metalloproteinase 3 (MMP-3) 0.216694 0.542692 137Matrix Metalloproteinase 7 (MMP-7) 0.303586 0.557821 138 MatrixMetalloproteinase 9 (MMP-9) 0.650263 0.500128 139 MatrixMetalloproteinase 9 total (MMP-9 total) 0.939975 0.507308 140 MatrixMetalloproteinase 10 (MMP-10) 0.352371 0.578462 141 Microalbumin0.195335 0.448077 142 Monocyte Chemotactic Protein 1 (MCP-1) 0.126570.584103 143 Monocyte Chemotactic Protein 2 (MCP-2) 0.427973 0.49 144Monocyte Chemotactic Protein 3 (MCP-3) NA NA 145 Monocyte ChemotacticProtein 4 (MCP-4) 0.693488 0.542949 146 Monokine Induced by GammaInterferon (MIG) 0.610324 0.534872 147 Myeloid Progenitor InhibitoryFactor 1 (MPIF-1) 0.278631 0.488974 148 Myeloperoxidase(MPO) 0.930550.521795 149 Myoglobin 0.056264 0.625256 150 Nerve Growth Factor beta(NGF-beta) NA NA 151 Neurofilament heavy polypeptide (NF-H) 0.299520.532179 152 Neuron Specific Enolase (NSE) 0.246881 0.449744 153Neuronal Cell Adhesion Molecule (NrCAM) 0.043976 0.605 154 Neuropilin-10.525837 0.531795 155 Neutrophil Activating Peptide 2 (NAP-2) 0.9006220.51141 156 Omentin 0.655919 0.524359 157 Osteocalcin 0.999932 0.495 158Osteopontin 0.97764 0.510256 159 Osteoprotegerin (OPG) 0.281271 0.54359160 P-Selectin 0.573835 0.506026 161 Pancreatic Polypeptide (PPP)0.279735 0.528205 162 Pancreatic secretory trypsin inhibitor (TATI)0.744834 0.534487 163 Paraoxonase-1 (PON1) 0.001273 0.645897 164Pepsinogen-I (PGI) 0.490286 0.527179 165 Periostin 0.410303 0.480513 166Pigment Epithelium Derived Factor (PEDF) 0.002423 0.641282 167 PlacentaGrowth Factor (PLGF) 0.063951 0.572564 168 Plasminogen ActivatorInhibitor 1 (PAI-1) 0.12149 0.587436 169 Platelet endothelial celladhesion molecule (PECAM-1) 0.839889 0.491282 170 Platelet DerivedGrowth Factor BB (PDGF-BB) 0.430758 0.535641 171 Prolactin (PRL)0.278799 0.562821 172 Prostate Specific Antigen Free (PSA-f) 0.2851990.51859 173 Protein DJ-1 (DJ-1) 0.241978 0.529231 174 Pulmonary andActivation Regulated Chemokine (PARC) 0.088935 0.592436 175 Pulmonarysurfactant associated protein D (SP-D) 0.057952 0.584615 176 Receptorfor advanced glycosylation end products (RAGE_ 0.008763 0.614744 177Resistin 0.300862 0.555385 178 S100 calcium binding protein B (S100B)0.304565 0.556026 179 Serum Amyloid A Protein (SAA) 0.226538 0.523077180 Serum Amyloid P Component (SAP) 0.077886 0.575897 181 Sex HormoneBinding Globulin (SHBG) 0.076998 0.576795 182 Sortilin 0.369815 0.536154183 ST2 0.906613 0.496795 184 Stem Cell Factor (SCF) 0.157098 0.542949185 Stromal cell derived factor 1 (SDF-1) 0.014087 0.631282 186Superoxide Dismutase 1 soluble (SOD-1) 0.955132 0.502308 187 T CellSpecific Protein RANTES (RANTES) 0.515373 0.544231 188 T LymphocyteSecreted Protein I 309 (I309) 0.222034 0.50641 189 Tamm Horsfall UrinaryGlycoprotein (THP) 0.863214 0.527051 190 Tenascin C (TN-C) 0.3477870.525641 191 Tetranectin 0.759074 0.518077 192 Thrombin ActivatableFibrinolysis (TAFI) 0.669463 0.529231 193 Thrombospondin-1 0.7316790.52641 194 Thymus and activation regulated chemokine (TARC) 0.2433710.546795 195 Thyroid Stimulating Hormone (TSH) 0.36033 0.495513 196Thyroxine Binding Globulin (TBG) 0.193485 0.586154 197 Tissue Inhibitorof Metalloproteinases 1 (TIMP-1) 0.51884 0.562436 198 Tissue Inhibitorof Metalloproteinases 2 (TIMP-2) 0.993488 0.51141 199 TNF RelatedApoptosis Inducing Ligand Receptor 3 (TRAIL-R3) 0.113077 0.565 200Transferrin receptor protein 1 (TFR1) 0.856601 0.516667 201 TransformingGrowth Factor beta 3 (TGF-beta3) NA NA 202 Tumor Necrosis Factor alpha(TNF-alpha) 0.312678 0.538718 203 Tumor Necrosis Factor beta (TNF-beta)NA NA 204 Tumor necrosis factor ligand superfamily member 12 (Tweak)0.951312 0.509359 205 Tumor necrosis factor ligand superfamily member 13(APRIL) 0.235847 0.557436 206 Tumor Necrosis Factor Receptor I (TNF-RI)0.329747 0.548718 207 Tumor necrosis factor receptor 2 (TNFR2) 0.2452710.546667 208 Vascular Cell Adhesion Molecule 1 (VCAM-1) 0.0091690.618718 209 Vascular Endothelial Growth Factor (VEGF) 0.032693 0.600513210 Visceral adipose tissue derived serpin A12 (Vaspin) 0.2275090.519487 211 Visfatin 0.591463 0.524615 212 Vitamin D Binding Protein(VDBP) 0.833476 0.526154 213 Vitronectin 0.571112 0.499231 214 vonWillebrand Factor (vWF) 0.313429 0.534872 215 YKL-40 0.803751 0.521026

TABLE 3 Ranked Biomarker List Biomarker Biomarker Accession Number SetNumber(s) Biomarker 1 1 NM_000446 Paraoxonase 1 (PON1) 2 1 NM_203377Myoglobin 3 1 NM_000602 Plasminogen Activator Inhibitor (PAI1) 4 1NM_003254 Tissue Inhibitor of Metalloproteinases 1 (TIMP1) 5 1 NM_000609Stromal Cell Derived Factor 1 (SDF1) 6 1 NM_002184 Interleukin 6Receptor Subunit Beta (IL6Rbeta) 7 1 NM_000100 Cystatin B 8 1 NG_001019,Immunoglobulin E (IgE) P01854 9 1 NM_006274 Macrophage InflammatoryProtein 3 beta (MIP3beta) 10 1 NM_001078 Vascular Cell Adhesion Molecule1 (VCAM1) 11 2 NM_002990 Macrophage Derived Chemokine (MDC) 12 2NM_001025366 Vascular Endothelial Growth Factor (VEGF) 13 2 NM_003665Ficolin 3 14 2 NG_001019, Immunoglobulin A (IgA) P01876 15 2 NM_000131Factor VII 16 2 NM_000565 Interleukin 6 Receptor (IL6R) 17 2 NM_001136Receptor for Advanced Glycosylation End Products (RAGE) 18 2 NM_006486Fibulin 1C (FIB1C) 19 2 NM_001302123 Interferon Inducible T Cell AlphaChemoattractant (ITAC) 20 2 NM_000515 Growth Hormone (GH) 21 3 NM_001945Heparin Binding EGF Like Growth Factor (HBEGF) 22 3 NM_001037132Neuronal Cell Adhesion Molecule (NrCAM) 23 3 NM_001511 Growth RegulatedAlpha Protein (GROalpha) 24 3 NM_004864 Growth Differentation Factor 15(GDF15) 25 3 NM_000222 Mast Stem Cell Growth Factor Receptor (SCFR) 26 3NM_004360 Cadherin 1 (Ecad) 27 3 NM_001097577 Angiogenin 28 3 NM_002959Sortilin 29 3 NM_001002235 Alpha 1 Antitrypsin (AAT) 30 3 NG_001019,Immunoglobulin M (IgM) P01871 31 3 NM_002988 Pulmonary and ActivationRegulated Chemokine (PARC) 32 3 NM_003019 Pulmonary SurfactantAssociated Protein D (SP-D) 33 3 NM_001145645 B Cell Activating Factor(BAFF) 34 3 NM_001124 Adrenomedullin (ADM) 35 3 NM_002615 PigmentEpithelium Derived Factor (PEDF) 36 3 NM_173841 Interleukin 1 ReceptorAntagonist (IL1ra) 37 3 NM_000354 Thyroxine Binding Globulin (TBG) 38 3NM_000477 Microalbumin 39 3 NM_000230 Leptin 40 3 NM_002986 Eotaxin 2 414 NM_000597 Insulin like Growth Factor Binding Protein 2 (IGFBP2) 42 4NM_020415 Resistin 43 4 NM_001909 Cathepsin D 44 4 NM_000450 E-Selectin45 4 NM_001276 YKL40 46 4 NM_020525 Interleukin 22 (IL22) 47 4 NM_004363Cacinoembryonic Antigen (CEA) 48 4 NM_000584 Interleukin 8 (IL8) 49 4NM_002456 Cancer Antigen 15-3 (CA 15-3) 50 4 NM_002303 Leptin Receptor(LeptinR) 51 4 NM_000207 Insulin 52 4 NM_002982 Monocyte ChemotacticProtein 1 (MCP1) 53 4 NM_000948 Prolactin (PRL) 54 4 NM_003278Tetranectin 55 4 NM_001712 Carcinoembryonic Antigen Related CellAdhesion Molecule 1 (CEACAM1) 56 4 NM_002989 6Ckine 57 4 NM_001639 SerumAmyloid P Component (SAP) 58 4 NM_002113 Complement Factor H RelatedProtein 1 (CFHR1) 59 4 NM_004590 Chemokine CC-4 (HCC-4) 60 4 NM-000064Complement C3 (C3) 61 5 NM_001134 Alpha Fetoprotein (AFP) 62 5NM_001146, Angiopoietin 1 (ANG-1) NM_139290 63 5 NM_001562 Interleukin18 (IL18) 64 5 NM_000177 Gelsolin 65 5 NM_002160 Tenascin C (TN-C) 66 5NM_000638 Vitronectin 67 5 NM_004048 Beta 2 Microglobulin (B2M) 68 5NM.003122 Pancreatic Secretory Trypsin Inhibitor (TATI) 69 5 NM_002422Matrix Metalloproteinase 3 (MMP3) 70 5 NM_017625 Omentin 71 5 NM_173042Interleukin 18 Binding Protein (IL 18bp) 72 5 NM_001647 Apolipoprotein D(ApoD) 73 5 NM_005408 Monoctye Chemotactic Protein 4 (MCP-4) 74 5NM_000041 Apolipoprotein E (Apo-E) 75 5 NM_016232 ST2 76 5 NM_003246Thrombospondin 1 77 5 NM_004123 Gastric Inhibitory Polypeptide (GIP) 785 NM_002423 Matrix Metalloproteinase 7 (MMP7) 79 5 NM_000201Intercellular Adhesion Molecule 1 (ICAM-1) 80 5 NM_012242 DickkopfRelated Protein 1 (DKK1)

TABLE 4 2 Biomarker Multivariate Analysis. Combination of Paraoxonase 1(PON1) and Analyte 2 AUC by AUC by random logistic Maximum Analyte 2forest regression AUC Myoglobin 0.52 0.68 0.68 Plasminogen ActivatorInhibitor (PAI1) 0.57 0.66 0.66 Tissue Inhibitor of Metalloproteinases 1(TIMP1) 0.59 0.65 0.65 Stromal Cell Derived Factor 1 (SDF1) 0.54 0.700.70 Interleukin 6 Receptor Subunit Beta (IL6Rbeta) 0.60 0.72 0.72Cystatin B 0.64 0.70 0.70 Immunoglobulin E (IgE) 0.55 0.68 0.68Macrophage Inflammatory Protein 3 beta (MIP3beta) 0.58 0.70 0.70Vascular Cell Adhesion Molecule 1 (VCAM1) 0.58 0.69 0.69 MacrophageDerived Chemokine (MDC) 0.63 0.71 0.71 Vascular Endothelial GrowthFactor (VEGF) 0.49 0.69 0.69 Ficolin 3 0.57 0.70 0.70 Immunoglobulin A(IgA) 0.54 0.70 0.70 Factor VII 0.69 0.70 0.70 Interleukin 6 Receptor(IL6R) 0.55 0.67 0.67 Receptor for Advanced Glycosylation End Products(RAGE) 0.48 0.69 0.69 Fibulin 1C (FIB1C) 0.52 0.66 0.66 InterferonInducble T Cell Alpha Chemoattractant (ITAC) 0.60 0.65 0.65 GrowthHormone (GH) 0.54 0.67 0.67 Heparin Binding EGF Like Growth Factor(HBEGF) 0.63 0.66 0.66 Neuronal Cell Adhesion Molecule (NrCAM) 0.44 0.680.68 Growth Regulated Alpha Protein (GROalpha) 0.54 0.64 0.64 GrowthDifferentation Factor 15 (GDF15) 0.58 0.65 0.65 Mast Stem Cell GrowthFactor Receptor (SCFR) 0.56 0.68 0.68 Cadherin 1 (Ecad) 0.61 0.65 0.65Angiogenin 0.55 0.66 0.66 Sortilin 0.60 0.66 0.66 Alpha 1 Antitrypsin(AAT) 0.54 0.66 0.66 Immunoglobulin M (IgM) 0.56 0.64 0.64 Pulmonary andActivation Regulated Chemokine (PARC) 0.68 0.67 0.68 PulmonarySurfactant Associated Protein D (SP-D) 0.52 0.65 0.65 B Cell ActivatingFactor (BAFF) 0.59 0.67 0.67 Adrenomedullin (ADM) 0.63 0.68 0.68 PigmentEpithelium Derived Factor (PEDF) 0.62 0.74 0.74 Interleukin 1 ReceptorAntagonist (IL1ra) 0.49 0.70 0.70 Thyrosine Binding Globulin (TBG) 0.550.67 0.67 Microalbumin 0.56 0.64 0.64 Leptin 0.57 0.68 0.68 Eotaxin 20.60 0.64 0.64 Insulin like Growth Factor Binding Protein 2 (IGFBP2)0.46 0.66 0.66 Resistin 0.52 0.66 0.66 Cathepsin D 0.60 0.70 0.70E-Selectin 0.52 0.69 0.69 YKL40 0.53 0.66 0.66 Interleukin 22 (IL22)0.53 0.67 0.67 Cacinoembryoinc Antigen (CEA) 0.46 0.64 0.64 Interleukin8 (IL8) 0.59 0.64 0.64 Cancer Antigen 15-3 (CA 15-3) 0.58 0.65 0.65Leptin Receptor (LeptinR) 0.55 0.65 0.65 Insulin 0.58 0.68 0.68 MonocyteChemotactic Protein 1 (MCP1) 0.61 0.69 0.69 Prolactin (PRL) 0.51 0.650.65 Tetranectin 0.47 0.65 0.65 Carcinoembryonic Antigen Related CellAdhesion Molecule 1 (CEACAM1) 0.61 0.65 0.65 6Ckine 0.52 0.66 0.66 SerumAmyloid P Component (SAP) 0.59 0.69 0.69 Complement Factor H RelatedProtein 1 (CFHR1) 0.53 0.65 0.65 Chemokine CC-4 (HCC-4) 0.58 0.65 0.65Complement C3 (C3) 0.56 0.67 0.67 Alpha Fetoprotein (AFP) 0.56 0.68 0.68Angiopoietin 1 (ANG-1) 0.51 0.64 0.64 Interleukin 18 (IL18) 0.62 0.680.68 Gelsolin 0.65 0.66 0.66 Tenascin C (TN-C) 0.59 0.66 0.66Vitronectin 0.55 0.68 0.68 Beta 2 Microglobulin (B2M) 0.60 0.66 0.66Pancreatic Secretory Trypsin Inhibitor (TATI) 0.55 0.64 0.64 MatrixMetailoproteinase 3 (MMP3) 0.60 0.66 0.66 Omentin 0.58 0.65 0.65Interleukin 18 Binding Protein (IL 18bp) 0.57 0.65 0.65 Apolipoprotein D(ApoD) 0.57 0.65 0.65 Monoctye Chemotactic Protein 4 (MCP-4) 0.54 0.650.65 Apolipoprotein E (Apo-E) 0.60 0.69 0.69 ST2 0.54 0.64 0.64Thrombospondin 1 0.50 0.65 0.65 Gastric Inhibitory Polypeptide (GIP)0.56 0.67 0.67 Matrix Metalloproteinase 7 (MMP7) 0.53 0.66 0.66Intercellular Adhesion Molecule 1 (ICAM-1) 0.51 0.65 0.65 DickkopfRelated Protein 1 (DKK1) 0.53 0.66 0.66

TABLE 5 2 Biomarker Multivariate Analysis. Combination of Myoglobin andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Plasminogen Activator Inhibitor (PAI1) 0.52 0.63 0.63Tissue Inhibitor of Metalloproteinases 1 (TIMP1) 0.61 0.62 0.62 StromalCell Derived Factor 1 (SDF1) 0.52 0.65 0.65 Interleukin 6 ReceptorSubunit Beta (IL6Rbeta) 0.48 0.67 0.67 Cystatin B 0.66 0.72 0.72Immunoglobulin E (IgE) 0.57 0.64 0.64 Macrophage Inflammatory Protein 3beta (MIP3beta) 0.62 0.67 0.67 Vascular Cell Adhesion Molecule 1 (VCAM1)0.51 0.63 0.63 Macrophage Derived Chemokine (MDC) 0.62 0.65 0.65Vascular Endothelial Growth Factor (VEGF) 0.51 0.65 0.65 Ficolin 3 0.520.64 0.64 Immunoglobulin A (IgA) 0.55 0.65 0.65 Factor VII 0.60 0.620.62 Interleukin 6 Receptor (IL6R) 0.49 0.61 0.61 Receptor for AdvancedGlycosylation End Products (RAGE) 0.58 0.63 0.63 Fibulin 1C (FIB1C) 0.520.62 0.62 Interferon Inducble T Cell Alpha Chemoattractant (ITAC) 0.560.61 0.61 Growth Hormone (GH) 0.49 0.64 0.64 Heparin Binding EGF LikeGrowth Factor (HBEGF) 0.55 0.63 0.63 Neuronal Cell Adhesion Molecule(NrCAM) 0.52 0.67 0.67 Growth Regulated Alpha Protein (GROalpha) 0.540.62 0.62 Growth Differentation Factor 15 (GDF15) 0.60 0.62 0.62 MastStem Cell Growth Factor Receptor (SCFR) 0.51 0.62 0.62 Cadherin 1 (Ecad)0.67 0.62 0.67 Angiogenin 0.49 0.63 0.63 Sortilin 0.49 0.62 0.62 Alpha 1Antitrypsin (AAT) 0.47 0.62 0.62 Immunoglobulin M (IgM) 0.59 0.61 0.61Pulmonary and Activation Regulated Chemokine (PARC) 0.61 0.67 0.67Pulmonary Surfactant Associated Protein D (SP-D) 0.51 0.62 0.62 B CellActivating Factor (BAFF) 0.53 0.63 0.63 Adrenomedullin (ADM) 0.56 0.660.66 Pigment Epithelium Derived Factor (PEDF) 0.57 0.69 0.69 Interleukin1 Receptor Antagonist (IL1ra) 0.62 0.70 0.70 Thyrosine Binding Globulin(TBG) 0.57 0.62 0.62 Microalbumin 0.61 0.62 0.62 Leptin 0.54 0.66 0.66Eotaxin 2 0.67 0.63 0.67 Insulin like Growth Factor Binding Protein 2(IGFBP2) 0.62 0.61 0.62 Resistin 0.54 0.66 0.66 Cathepsin D 0.64 0.660.66 E-Selectin 0.51 0.68 0.68 YKL40 0.53 0.62 0.62 Interleukin 22(IL22) 0.56 0.62 0.62 Cacinoembryonic Antigen (CEA) 0.54 0.64 0.64Interleukin 8 (IL8) 0.64 0.62 0.64 Cancer Antigen 15-3 (CA 15-3) 0.490.63 0.63 Leptin Receptor (LeptinR) 0.47 0.64 0.64 Insulin 0.49 0.650.65 Monocyte Chemotactic Protein 1 (MCP1) 0.56 0.65 0.65 Prolactin(PRL) 0.51 0.63 0.63 Tetranectin 0.57 0.62 0.62 Carcinoembryonic AntigenRelated Cell Adhesion 0.59 0.62 0.62 Molecule 1 (CEACAM1) 6Ckine 0.490.65 0.65 Serum Amyloid P Component (SAP) 0.53 0.62 0.62 ComplementFactor H Related Protein 1 (CFHR1) 0.54 0.63 0.63 Chemokine CC-4 (HCC-4)0.57 0.62 0.62 Complement C3 (C3) 0.58 0.61 0.61 Alpha Fetoprotein (AFP)0.61 0.65 0.65 Angiopoietin 1 (ANG-1) 0.57 0.62 0.62 Interleukin 18(IL18) 0.58 0.68 0.68 Gelsolin 0.54 0.61 0.61 Tenascin C (TN-C) 0.470.62 0.62 Vitronectin 0.60 0.62 0.62 Beta 2 Microglobulin (B2M) 0.560.64 0.64 Pancreatic Secretory Trypsin Inhibitor (TATI) 0.56 0.62 0.62Matrix Metalloproteinase 3 (MMP3) 0.60 0.63 0.63 Omentin 0.57 0.62 0.62Interleukin 18 Binding Protein (IL 18bp) 0.53 0.66 0.66 Apolipoprotein D(ApoD) 0.58 0.62 0.62 Monoctye Chemotactic Protein 4 (MCP-4) 0.50 0.630.63 Apolipoprotein E (Apo-E) 0.48 0.64 0.64 ST2 0.52 0.63 0.63Thrombospondin 1 0.52 0.62 0.62 Gastric Inhibitory Polypeptide (GIP)0.48 0.62 0.62 Matrix Metalloproteinase 7 (MMP7) 0.52 0.61 0.61Intercellular Adhesion Molecule 1 (ICAM-1) 0.53 0.64 0.64 DickkopfRelated Protein 1 (DKK1) 0.53 0.61 0.61

TABLE 6 2 Biomarker Multivariate Analysis. Combination of PlasminogenActivator Inhibitor (PAI1) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Tissue Inhibitor ofMetalloproteinases 1 (TIMP1) 0.70 0.64 0.70 Stromal Cell Derived Factor1 (SDF1) 0.57 0.64 0.64 Interleukin 6 Receptor Subunit Beta (IL6Rbeta)0.60 0.65 0.65 Cystatin B 0.61 0.66 0.66 Immunoglobulin E (IgE) 0.540.64 0.64 Macrophage Inflammatory Protein 3 beta (MIP3beta) 0.66 0.640.66 Vascular Cell Adhesion Molecule 1 (VCAM1) 0.56 0.64 0.64 MacrophageDerived Chemokine (MDC) 0.65 0.65 0.65 Vascular Endothelial GrowthFactor (VEGF) 0.55 0.61 0.61 Ficolin 3 0.62 0.63 0.63 Immunoglobulin A(IgA) 0.53 0.64 0.64 Factor VII 0.60 0.63 0.63 Interleukin 6 Receptor(IL6R) 0.55 0.59 0.59 Receptor for Advanced Glycosylation End Products(RAGE) 0.60 0.64 0.64 Fibulin 1C (FIB1C) 0.49 0.59 0.59 InterferonInducble T Cell Alpha Chemoattractant (ITAC) 0.54 0.58 0.58 GrowthHormone (GH) 0.55 0.62 0.62 Heparin Binding EGF Like Growth Factor(HBEGF) 0.52 0.62 0.62 Neuronal Cell Adhesion Molecule (NrCAM) 0.54 0.650.65 Growth Regulated Alpha Protein (GROalpha) 0.49 0.59 0.59 GrowthDifferentation Factor 15 (GDF15) 0.59 0.59 0.59 Mast Stem Cell GrowthFactor Receptor (SCFR) 0.56 0.62 0.62 Cadherin 1 (Ecad) 0.48 0.59 0.59Angiogenin 0.57 0.61 0.61 Sortilin 0.49 0.61 0.61 Alpha 1 Antitrypsin(AAT) 0.56 0.59 0.59 Immunoglobulin M (IgM) 0.56 0.59 0.59 Pulmonary andActivation Regulated Chemokine (PARC) 0.61 0.63 0.63 PulmonarySurfactant Associated Protein D (SP-D) 0.54 0.59 0.59 B Cell ActivatingFactor (BAFF) 0.52 0.60 0.60 Adrenomedullin (ADM) 0.57 0.64 0.64 PigmentEpithelium Derived Factor (PEDF) 0.60 0.68 0.68 Interleukin 1 ReceptorAntagonist (IL1ra) 0.52 0.64 0.64 Thyrosine Binding Globulin (TBG) 0.490.62 0.62 Microalbumin 0.59 0.58 0.59 Leptin 0.47 0.64 0.64 Eotaxin 20.56 0.58 0.58 Insulin like Growth Factor Binding Protein 2 (IGFBP2)0.50 0.59 0.59 Resistin 0.54 0.61 0.61 Cathepsin D 0.60 0.66 0.66E-Selectin 0.50 0.64 0.64 YKL40 0.52 0.59 0.59 Interleukin 22 (IL22)0.54 0.61 0.61 Cacinoembryonic Antigen (CEA) 0.52 0.60 0.60 Interleukin8 (IL8) 0.60 0.59 0.60 Cancer Antigen 15-3 (CA 15-3) 0.54 0.59 0.59Leptin Receptor (LeptinR) 0.49 0.60 0.60 Insulin 0.58 0.64 0.64 MonocyteChemotactic Protein 1 (MCP1) 0.55 0.65 0.65 Prolactin (PRL) 0.53 0.580.58 Tetranectin 0.58 0.59 0.59 Carcinoembryonic Antigen Related CellAdhesion Molecule 1 0.50 0.58 0.58 (CEACAM1) 6Ckine 0.60 0.64 0.64 SerumAmyloid P Component (SAP) 0.64 0.62 0.64 Complement Factor H RelatedProtein 1 (CFHR1) 0.51 0.59 0.59 Chemokine CC-4 (HCC-4) 0.48 0.58 0.58Complement C3 (C3) 0.55 0.59 0.59 Alpha Fetoprotein (AFP) 0.52 0.58 0.58Angiopoietin 1 (ANG-1) 0.54 0.59 0.59 Interleukin 18 (IL18) 0.68 0.650.68 Gelsolin 0.52 0.60 0.60 Tenascin C (TN-C) 0.54 0.60 0.60Vitronectin 0.62 0.60 0.62 Beta 2 Microglobulin (B2M) 0.57 0.61 0.61Pancreatic Secretory Trypsin Inhibitor (TATI) 0.60 0.59 0.60 MatrixMetalloproteinase 3 (MMP3) 0.67 0.62 0.67 Omentin 0.58 0.60 0.60Interleukin 18 Binding Protein (IL 18bp) 0.53 0.60 0.60 Apolipoprotein D(ApoD) 0.60 0.60 0.60 Monoctye Chemotactic Protein 4 (MCP-4) 0.53 0.580.58 Apolipoprotein E (Apo-E) 0.57 0.64 0.64 ST2 0.51 0.59 0.59Thrombospondin 1 0.57 0.61 0.61 Gastric Inhibitory Polypeptide (GIP)0.56 0.61 0.61 Matrix Metalloproteinase 7 (MMP7) 0.61 0.61 0.61Intercellular Adhesion Molecule 1 (ICAM-1) 0.53 0.59 0.59 DickkopfRelated Protein 1 (DKK1) 0.50 0.62 0.62

TABLE 7 2 Biomarker Multivariate Analysis. Combination of TissueInhibitor of Metalloproteinases 1 (TIMP1) and Analyte 2 AUC by AUC byrandom logistic Maximum Analyte 2 forest regression AUC Stromal CellDerived Factor 1 (SDF1) 0.65 0.67 0.67 Interleukin 6 Receptor SubunitBeta (IL6Rbeta) 0.59 0.63 0.63 Cystatin B 0.60 0.64 0.64 ImmunoglobulinE (IgE) 0.65 0.63 0.65 Macrophage Inflammatory Protein 3 beta (MIP3beta)0.64 0.62 0.64 Vascular Cell Adhesion Molecule 1 (VCAM1) 0.66 0.62 0.66Macrophage Derived Chemokine (MDC) 0.62 0.63 0.63 Vascular EndothelialGrowth Factor (VEGF) 0.57 0.62 0.62 Ficolin 3 0.66 0.62 0.66Immunoglobulin A (IgA) 0.53 0.59 0.59 Factor VII 0.60 0.59 0.60Interleukin 6 Receptor (IL6R) 0.60 0.60 0.60 Receptor for AdvancedGlycosylation End Products (RAGE) 0.67 0.63 0.67 Fibulin 1C (FIB1C) 0.620.56 0.62 Interferon lnducble T Cell Alpha Chemoattractant (ITAC) 0.640.56 0.64 Growth Hormone (GH) 0.59 0.60 0.60 Heparin Binding EGF LikeGrowth Factor (HBEGF) 0.67 0.53 0.67 Neuronal Cell Adhesion Molecule(NrCAM) 0.63 0.59 0.63 Growth Regulated Alpha Protein (GROalpha) 0.610.57 0.61 Growth Differentation Factor 15 (GDF15) 0.68 0.59 0.68 MastStem Cell Growth Factor Receptor (SCFR) 0.55 0.60 0.60 Cadherin 1 (Ecad)0.65 0.58 0.65 Angiogenin 0.63 0.58 0.63 Sortilin 0.60 0.54 0.60 Alpha 1Antitrypsin (AAT) 0.67 0.56 0.67 Immunoglobulin M (IgM) 0.63 0.55 0.63Pulmonary and Activation Regulated Chemokine (PARC) 0.68 0.59 0.68Pulmonary Surfactant Associated Protein D (SP-D) 0.58 0.56 0.58 B CellActivating Factor (BAFF) 0.60 0.60 0.60 Adrenomedullin (ADM) 0.62 0.630.63 Pigment Epithelium Derived Factor (PEDF) 0.65 0.64 0.65 Interleukin1 Receptor Antagonist (IL1ra) 0.59 0.59 0.59 Thyrosine Binding Globulin(TBG) 0.56 0.59 0.59 Microalbumin 0.71 0.57 0.71 Leptin 0.53 0.60 0.60Eotaxin 2 0.66 0.58 0.66 Insulin like Growth Factor Binding Protein 2(IGFBP2) 0.65 0.57 0.65 Resistin 0.55 0.55 0.55 Cathepsin D 0.59 0.620.62 E-Selectin 0.61 0.63 0.63 YKL40 0.57 0.56 0.57 Interleukin 22(IL22) 0.54 0.57 0.57 Cacinoembryonic Antigen (CEA) 0.57 0.58 0.58Interleukin 8 (IL8) 0.60 0.56 0.60 Cancer Antigen 15-3 (CA 15-3) 0.640.56 0.64 Leptin Receptor (LeptinR) 0.58 0.60 0.60 Insulin 0.64 0.600.64 Monocyte Chemotactic Protein 1 (MCP1) 0.57 0.59 0.59 Prolactin(PRL) 0.62 0.56 0.62 Tetranectin 0.57 0.58 0.58 Carcinoembryonic AntigenRelated Cell Adhesion 0.65 0.58 0.65 Molecule 1 (CEACAM1) 6Ckine 0.660.61 0.66 Serum Amyloid P Component (SAP) 0.69 0.58 0.69 ComplementFactor H Related Protein 1 (CFHR1) 0.60 0.55 0.60 Chemokine CC-4 (HCC-4)0.62 0.54 0.62 Complement C3 (C3) 0.64 0.46 0.64 Alpha Fetoprotein (AFP)0.59 0.60 0.60 Angiopoietin 1 (ANG-1) 0.62 0.56 0.62 Interleukin 18(IL18) 0.67 0.65 0.67 Gelsolin 0.61 0.56 0.61 Tenascin C (TN-C) 0.640.55 0.64 Vitronectin 0.69 0.55 0.69 Beta 2 Microglobulin (B2M) 0.600.56 0.60 Pancreatic Secretory Trypsin Inhibitor (TATI) 0.63 0.56 0.63Matrix Metalloproteinase 3 (MMP3) 0.65 0.56 0.65 Omentin 0.58 0.57 0.58Interleukin 18 Binding Protein (IL 18bp) 0.59 0.60 0.60 Apolipoprotein D(ApoD) 0.70 0.57 0.70 Monoctye Chemotactic Protein 4 (MCP-4) 0.60 0.560.60 Apolipoprotein E (Apo-E) 0.71 0.61 0.71 ST2 0.64 0.56 0.64Thrombospondin 1 0.67 0.56 0.67 Gastric Inhibitory Polypeptide (GIP)0.58 0.58 0.58 Matrix Metalloproteinase 7 (MMP7) 0.61 0.56 0.61Intercellular Adhesion Molecule 1 (ICAM-1) 0.57 0.57 0.57 DickkopfRelated Protein 1 (DKK1) 0.57 0.53 0.57

TABLE 8 2 Biomarker Multivariate Analysis. Combination of Stromel CellDerived Factor 1 (SDF1) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Interleukin 6 Receptor SubunitBeta (IL6Rbeta) 0.53 0.68 0.68 Cystatin B 0.59 0.68 0.68 ImmunoglobulinE (IgE) 0.65 0.69 0.69 Macrophage Inflammatory Protein 3 beta (MIP3beta)0.62 0.67 0.67 Vascular Cell Adhesion Molecule 1 (VCAM1) 0.56 0.68 0.68Macrophage Derived Chemokine (MDC) 0.61 0.70 0.70 Vascular EndothelialGrowth Factor (VEGF) 0.57 0.66 0.66 Ficolin 3 0.52 0.64 0.64Immunoglobulin A (IgA) 0.50 0.69 0.69 Factor VII 0.56 0.65 0.65Interleukin 6 Receptor (IL6R) 0.47 0.65 0.65 Receptor for AdvancedGlycosylation End Products (RAGE) 0.55 0.66 0.66 Fibulin 1C (FIB1C) 0.520.64 0.64 Interferon lnducble T Cell Alpha Chemoattractant (ITAC) 0.540.64 0.64 Growth Hormone (GH) 0.59 0.66 0.66 Heparin Binding EGF LikeGrowth Factor (HBEGF) 0.58 0.66 0.66 Neuronal Cell Adhesion Molecule(NrCAM) 0.60 0.64 0.64 Growth Regulated Alpha Protein (GROalpha) 0.600.63 0.63 Growth Differentation Factor 15 (GDF15) 0.68 0.64 0.68 MastStem Cell Growth Factor Receptor (SCFR) 0.51 0.66 0.66 Cadherin 1 (Ecad)0.51 0.64 0.64 Angiogenin 0.50 0.64 0.64 Sortilin 0.50 0.65 0.65 Alpha 1Antitrypsin (AAT) 0.57 0.64 0.64 Immunoglobulin M (IgM) 0.56 0.63 0.63Pulmonary and Activation Regulated Chemokine (PARC) 0.59 0.68 0.68Pulmonary Surfactant Associated Protein D (SP-D) 0.52 0.63 0.63 B CellActivating Factor (BAFF) 0.52 0.65 0.65 Adrenomedullin (ADM) 0.52 0.660.66 Pigment Epithelium Derived Factor (PEDF) 0.54 0.68 0.68 Interleukin1 Receptor Antagonist (IL1ra) 0.56 0.68 0.68 Thyrosine Binding Globulin(TBG) 0.49 0.65 0.65 Microalbumin 0.63 0.63 0.63 Leptin 0.62 0.66 0.66Eotaxin 2 0.48 0.63 0.63 Insulin like Growth Factor Binding Protein 2(IGFBP2) 0.47 0.63 0.63 Resistin 0.49 0.66 0.66 Cathepsin D 0.53 0.680.68 E-Selectin 0.51 0.69 0.69 YKL40 0.52 0.63 0.63 Interleukin 22(IL22) 0.50 0.66 0.66 Cacinoembryonic Antigen (CEA) 0.52 0.63 0.63Interleukin 8 (IL8) 0.56 0.64 0.64 Cancer Antigen 15-3 (CA 15-3) 0.540.63 0.63 Leptin Receptor (LeptinR) 0.55 0.64 0.64 Insulin 0.54 0.660.66 Monocyte Chemotactic Protein 1 (MCP1) 0.55 0.66 0.66 Prolactin(PRL) 0.55 0.65 0.65 Tetranectin 0.55 0.64 0.64 Carcinoembryonic AntigenRelated Cell Adhesion Molecule 1 0.57 0.64 0.64 (CEACAM1) 6Ckine 0.510.67 0.67 Serum Amyloid P Component (SAP) 0.44 0.64 0.64 ComplementFactor H Related Protein 1 (CFHR1) 0.52 0.63 0.63 Chemokine CC-4 (HCC-4)0.52 0.63 0.63 Complement C3 (C3) 0.47 0.64 0.64 Alpha Fetoprotein (AFP)0.50 0.64 0.64 Angiopoietin 1 (ANG-1) 0.55 0.63 0.63 Interleukin 18(IL18) 0.55 0.69 0.69 Gelsolin 0.59 0.64 0.64 Tenascin C (TN-C) 0.500.64 0.64 Vitronectin 0.44 0.63 0.63 Beta 2 Microglobulin (B2M) 0.530.63 0.63 Pancreatic Secretory Trypsin Inhibitor (TATI) 0.53 0.64 0.64Matrix Metalloproteinase 3 (MMP3) 0.65 0.64 0.65 Omentin 0.52 0.63 0.63Interleukin 18 Binding Protein (IL 18bp) 0.56 0.67 0.67 Apolipoprotein D(ApoD) 0.63 0.63 0.63 Monoctye Chemotactic Protein 4 (MCP-4) 0.60 0.660.66 Apolipoprotein E (Apo-E) 0.46 0.67 0.67 ST2 0.46 0.63 0.63Thrombospondin 1 0.51 0.64 0.64 Gastric Inhibitory Polypeptide (GIP)0.56 0.65 0.65 Matrix Metalloproteinase 7 (MMP7) 0.60 0.67 0.67Intercellular Adhesion Molecule 1 (ICAM-1) 0.52 0.63 0.63 DickkopfRelated Protein 1 (DKK1) 0.59 0.66 0.66

TABLE 9 2 Biomarker Multivariate Analysis. Combination of Interleukin 6Receptor Subunit Beta (IL6Rbeta) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC Cystatin B 0.52 0.660.66 Immunoglobulin E (IgE) 0.50 0.68 0.68 Macrophage InflammatoryProtein 3 beta (MIP3beta) 0.57 0.69 0.69 Vascular Cell Adhesion Molecule1 (VCAM1) 0.49 0.66 0.66 Macrophage Derived Chemokine (MDC) 0.52 0.680.68 Vascular Endothelial Growth Factor (VEGF) 0.56 0.66 0.66 Ficolin 30.51 0.66 0.66 Immunoglobulin A (IgA) 0.57 0.67 0.67 Factor VII 0.530.65 0.65 Interleukin 6 Receptor (IL6R) 0.58 0.65 0.65 Receptor forAdvanced Glycosylation End Products (RAGE) 0.50 0.69 0.69 Fibulin 1C(FIB1C) 0.52 0.63 0.63 Interferon Inducble T Cell Alpha Chemoattractant(ITAC) 0.71 0.62 0.71 Growth Hormone (GH) 0.54 0.66 0.66 Heparin BindingEGF Like Growth Factor (HBEGF) 0.47 0.63 0.63 Neuronal Cell AdhesionMolecule (NrCAM) 0.59 0.68 0.68 Growth Regulated Alpha Protein(GROalpha) 0.53 0.64 0.64 Growth Differentation Factor 15 (GDF15) 0.600.64 0.64 Mast Stem Cell Growth Factor Receptor (SCFR) 0.55 0.66 0.66Cadherin 1 (Ecad) 0.48 0.63 0.63 Angiogenin 0.46 0.63 0.63 Sortilin 0.510.65 0.65 Alpha 1 Antitrypsin (AAT) 0.49 0.66 0.66 Immunoglobulin M(IgM) 0.52 0.63 0.63 Pulmonary and Activation Regulated Chemokine (PARC)0.50 0.66 0.66 Pulmonary Surfactant Associated Protein D (SP-D) 0.500.63 0.63 B Cell Activating Factor (BAFF) 0.51 0.66 0.66 Adrenomedullin(ADM) 0.46 0.68 0.68 Pigment Epithelium Derived Factor (PEDF) 0.50 0.680.68 Interleukin 1 Receptor Antagonist (IL1ra) 0.50 0.69 0.69 ThyrosineBinding Globulin (TBG) 0.61 0.64 0.64 Microalbumin 0.61 0.67 0.67 Leptin0.54 0.66 0.66 Eotaxin 2 0.60 0.64 0.64 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.51 0.63 0.63 Resistin 0.51 0.64 0.64Cathepsin D 0.48 0.64 0.64 E-Selectin 0.59 0.65 0.65 YKL40 0.61 0.630.63 Interleukin 22 (IL22) 0.45 0.64 0.64 Cacinoembryonic Antigen (CEA)0.56 0.65 0.65 Interleukin 8 (IL8) 0.55 0.63 0.63 Cancer Antigen 15-3(CA 15-3) 0.56 0.63 0.63 Leptin Receptor (LeptinR) 0.57 0.64 0.64Insulin 0.53 0.66 0.66 Monocyte Chemotactic Protein 1 (MCP1) 0.54 0.650.65 Prolactin (PRL) 0.58 0.64 0.64 Tetranectin 0.53 0.64 0.64Carcinoembryonic Antigen Related Cell Adhesion 0.50 0.65 0.65 Molecule 1(CEACAM1) 6Ckine 0.53 0.64 0.64 Serum Amyloid P Component (SAP) 0.550.66 0.66 Complement Factor H Related Protein 1 (CFHR1) 0.55 0.63 0.63Chemokine CC-4 (HCC-4) 0.50 0.63 0.63 Complement C3 (C3) 0.50 0.63 0.63Alpha Fetoprotein (AFP) 0.51 0.67 0.67 Angiopoietin 1 (ANG-1) 0.52 0.630.63 Interleukin 18 (IL18) 0.57 0.68 0.68 Gelsolin 0.57 0.63 0.63Tenascin C (TN-C) 0.55 0.64 0.64 Vitronectin 0.54 0.63 0.63 Beta 2Microglobulin (B2M) 0.54 0.65 0.65 Pancreatic Secretory TrypsinInhibitor (TATI) 0.51 0.63 0.63 Matrix Metalloproteinase 3 (MMP3) 0.650.65 0.65 Omentin 0.59 0.63 0.63 Interleukin 18 Binding Protein (IL18bp) 0.51 0.66 0.66 Apolipoprotein D (ApoD) 0.62 0.63 0.63 MonoctyeChemotactic Protein 4 (MCP-4) 0.54 0.63 0.63 Apolipoprotein E (Apo-E)0.51 0.68 0.68 ST2 0.52 0.63 0.63 Thrombospondin 1 0.50 0.63 0.63Gastric Inhibitory Polypeptide (GIP) 0.46 0.65 0.65 MatrixMetalloproteinase 7 (MMP7) 0.49 0.64 0.64 Intercellular AdhesionMolecule 1 (ICAM-1) 0.54 0.63 0.63 Dickkopf Related Protein 1 (DKK1)0.63 0.64 0.64

TABLE 10 2 Biomarker Multivariate Analysis. Combination of Cystatin Band Analyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Immunoglobulin E (IgE) 0.61 0.67 0.67 MacrophageInflammatory Protein 3 beta (MIP3beta) 0.74 0.64 0.74 Vascular CellAdhesion Molecule 1 (VCAM1) 0.66 0.68 0.68 Macrophage Derived Chemokine(MDC) 0.59 0.64 0.64 Vascular Endothelial Growth Factor (VEGF) 0.63 0.690.69 Ficolin 3 0.58 0.64 0.64 Immunoglobulin A (IgA) 0.53 0.65 0.65Factor VII 0.65 0.65 0.65 Interleukin 6 Receptor (IL6R) 0.50 0.65 0.65Receptor for Advanced Glycosylation End Products (RAGE) 0.65 0.67 0.67Fibulin 1C (FIB1C) 0.64 0.64 0.64 Interferon lnducble T Cell AlphaChemoattractant (ITAC) 0.66 0.64 0.66 Growth Hormone (GH) 0.54 0.64 0.64Heparin Binding EGF Like Growth Factor (HBEGF) 0.62 0.64 0.64 NeuronalCell Adhesion Molecule (NrCAM) 0.68 0.68 0.68 Growth Regulated AlphaProtein (GROalpha) 0.63 0.64 0.64 Growth Differentation Factor 15(GDF15) 0.64 0.63 0.64 Mast Stem Cell Growth Factor Receptor (SCFR) 0.580.65 0.65 Cadherin 1 (Ecad) 0.64 0.64 0.64 Angiogenin 0.59 0.63 0.63Sortilin 0.61 0.63 0.63 Alpha 1 Antitrypsin (AAT) 0.66 0.67 0.67Immunoglobulin M (IgM) 0.69 0.65 0.69 Pulmonary and Activation RegulatedChemokine (PARC) 0.62 0.65 0.65 Pulmonary Surfactant Associated ProteinD (SP-D) 0.58 0.64 0.64 B Cell Activating Factor (BAFF) 0.63 0.64 0.64Adrenomedullin (ADM) 0.60 0.67 0.67 Pigment Epithelium Derived Factor(PEDF) 0.61 0.66 0.66 Interleukin 1 Receptor Antagonist (IL1ra) 0.580.64 0.64 Thyrosine Binding Globulin (TBG) 0.53 0.64 0.64 Microalbumin0.67 0.64 0.67 Leptin 0.48 0.64 0.64 Eotaxin 2 0.64 0.64 0.64 Insulinlike Growth Factor Binding Protein 2 (IGFBP2) 0.63 0.63 0.63 Resistin0.55 0.64 0.64 Cathepsin D 0.63 0.64 0.64 E-Selectin 0.58 0.66 0.66YKL40 0.58 0.64 0.64 Interleukin 22 (IL22) 0.52 0.63 0.63Cacinoembryonic Antigen (CEA) 0.56 0.63 0.63 Interleukin 8 (IL8) 0.640.65 0.65 Cancer Antigen 15-3 (CA 15-3) 0.54 0.64 0.64 Leptin Receptor(LeptinR) 0.56 0.64 0.64 Insulin 0.63 0.65 0.65 Monocyte ChemotacticProtein 1 (MCP1) 0.66 0.65 0.66 Prolactin (PRL) 0.61 0.63 0.63Tetranectin 0.56 0.64 0.64 Carcinoembryonic Antigen Related CellAdhesion 0.61 0.64 0.64 Molecule 1 (CEACAM1) 6Ckine 0.59 0.63 0.63 SerumAmyloid P Component (SAP) 0.61 0.62 0.62 Complement Factor H RelatedProtein 1 (CFHR1) 0.43 0.64 0.64 Chemokine CC-4 (HCC-4) 0.59 0.64 0.64Complement C3 (C3) 0.59 0.64 0.64 Alpha Fetoprotein (AFP) 0.57 0.66 0.66Angiopoietin 1 (ANG-1) 0.59 0.64 0.64 Interleukin 18 (IL18) 0.62 0.660.66 Gelsolin 0.66 0.66 0.66 Tenascin C (TN-C) 0.58 0.62 0.62Vitronectin 0.59 0.65 0.65 Beta 2 Microglobulin (B2M) 0.56 0.64 0.64Pancreatic Secretory Trypsin Inhibitor (TATI) 0.59 0.64 0.64 MatrixMetalloproteinase 3 (MMP3) 0.64 0.64 0.64 Omentin 0.51 0.64 0.64Interleukin 18 Binding Protein (IL 18bp) 0.63 0.64 0.64 Apolipoprotein D(ApoD) 0.67 0.63 0.67 Monoctye Chemotactic Protein 4 (MCP-4) 0.46 0.640.64 Apolipoprotein E (Apo-E) 0.57 0.64 0.64 ST2 0.57 0.64 0.64Thrombospondin 1 0.58 0.64 0.64 Gastric Inhibitory Polypeptide (GIP)0.55 0.65 0.65 Matrix Metalloproteinase 7 (MMP7) 0.67 0.62 0.67Intercellular Adhesion Molecule 1 (ICAM-1) 0.57 0.64 0.64 DickkopfRelated Protein 1 (DKK1) 0.54 0.64 0.64

TABLE 11 2 Biomarker Multivariate Analysis. Combination ofImmunoglobulin E (IgE) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Macrophage Inflammatory Protein3 beta (MIP3beta) 0.54 0.63 0.63 Vascular Cell Adhesion Molecule 1(VCAM1) 0.54 0.67 0.67 Macrophage Derived Chemokine (MDC) 0.48 0.64 0.64Vascular Endothelial Growth Factor (VEGF) 0.46 0.65 0.65 Ficolin 3 0.510.64 0.64 Immunoglobulin A (IgA) 0.51 0.66 0.66 Factor VII 0.63 0.650.65 Interleukin 6 Receptor (IL6R) 0.53 0.61 0.61 Receptor for AdvancedGlycosylation End Products (RAGE) 0.56 0.63 0.63 Fibulin 1C (FIB1C) 0.550.63 0.63 Interferon lnducble T Cell Alpha Chemoattractant (ITAC) 0.600.58 0.60 Growth Hormone (GH) 0.52 0.66 0.66 Heparin Binding EGF LikeGrowth Factor (HBEGF) 0.52 0.61 0.61 Neuronal Cell Adhesion Molecule(NrCAM) 0.61 0.63 0.63 Growth Regulated Alpha Protein (GROalpha) 0.560.64 0.64 Growth Differentation Factor 15 (GDF15) 0.55 0.60 0.60 MastStem Cell Growth Factor Receptor (SCFR) 0.52 0.63 0.63 Cadherin 1 (Ecad)0.54 0.60 0.60 Angiogenin 0.48 0.63 0.63 Sortilin 0.49 0.62 0.62 Alpha 1Antitrypsin (AAT) 0.56 0.60 0.60 Immunoglobulin M (IgM) 0.57 0.62 0.62Pulmonary and Activation Regulated Chemokine (PARC) 0.65 0.64 0.65Pulmonary Surfactant Associated Protein D (SP-D) 0.50 0.60 0.60 B CellActivating Factor (BAFF) 0.61 0.64 0.64 Adrenomedullin (ADM) 0.55 0.690.69 Pigment Epithelium Derived Factor (PEDF) 0.63 0.69 0.69 Interleukin1 Receptor Antagonist (IL1ra) 0.51 0.65 0.65 Thyrosine Binding Globulin(TBG) 0.58 0.66 0.66 Microalbumin 0.59 0.61 0.61 Leptin 0.56 0.66 0.66Eotaxin 2 0.62 0.62 0.62 Insulin like Growth Factor Binding Protein 2(IGFBP2) 0.59 0.62 0.62 Resistin 0.52 0.59 0.59 Cathepsin D 0.57 0.670.67 E-Selectin 0.55 0.66 0.66 YKL40 0.62 0.58 0.62 Interleukin 22(IL22) 0.54 0.59 0.59 Cacinoembryonic Antigen (CEA) 0.51 0.60 0.60Interleukin 8 (IL8) 0.62 0.59 0.62 Cancer Antigen 15-3 (CA 15-3) 0.530.60 0.60 Leptin Receptor (LeptinR) 0.59 0.66 0.66 Insulin 0.55 0.680.68 Monocyte Chemotactic Protein 1 (MCP1) 0.56 0.63 0.63 Prolactin(PRL) 0.54 0.63 0.63 Tetranectin 0.50 0.59 0.59 Carcinoembryonic AntigenRelated Cell Adhesion 0.50 0.61 0.61 Molecule 1 (CEACAM1) 6Ckine 0.620.66 0.66 Serum Amyloid P Component (SAP) 0.57 0.65 0.65 ComplementFactor H Related Protein 1 (CFHR1) 0.52 0.59 0.59 Chemokine CC-4 (HCC-4)0.66 0.59 0.66 Complement C3 (C3) 0.59 0.60 0.60 Alpha Fetoprotein (AFP)0.56 0.59 0.59 Angiopoietin 1 (ANG-1) 0.54 0.62 0.62 Interleukin 18(IL18) 0.64 0.68 0.68 Gelsolin 0.56 0.60 0.60 Tenascin C (TN-C) 0.510.61 0.61 Vitronectin 0.68 0.60 0.68 Beta 2 Microglobulin (B2M) 0.610.63 0.63 Pancreatic Secretory Trypsin Inhibitor (TATI) 0.57 0.58 0.58Matrix Metalloproteinase 3 (MMP3) 0.62 0.62 0.62 Omentin 0.52 0.62 0.62Interleukin 18 Binding Protein (IL 18bp) 0.65 0.65 0.65 Apolipoprotein D(ApoD) 0.64 0.65 0.65 Monoctye Chemotactic Protein 4 (MCP-4) 0.54 0.580.58 Apolipoprotein E (Apo-E) 0.59 0.64 0.64 ST2 0.59 0.59 0.59Thrombospondin 1 0.56 0.58 0.58 Gastric Inhibitory Polypeptide (GIP)0.57 0.64 0.64 Matrix Metalloproteinase 7 (MMP7) 0.64 0.62 0.64Intercellular Adhesion Molecule 1 (ICAM-1) 0.57 0.59 0.59 DickkopfRelated Protein 1 (DKK1) 0.51 0.57 0.57

TABLE 12 2 Biomarker Multivariate Analysis. Combination of MacrophageInflammatory Protein 3 beta (MIP3beta) and Analyte 2 AUC by AUC byrandom logistic Maximum Analyte 2 forest regression AUC Vascular CellAdhesion Molecule 1 (VCAM1) 0.60 0.65 0.65 Macrophage Derived Chemokine(MDC) 0.57 0.65 0.65 Vascular Endothelial Growth Factor (VEGF) 0.57 0.650.65 Ficolin 3 0.63 0.66 0.66 Immunoglobulin A (IgA) 0.50 0.63 0.63Factor VII 0.61 0.61 0.61 Interleukin 6 Receptor (IL6R) 0.59 0.65 0.65Receptor for Advanced Glycosylation End Products (RAGE) 0.63 0.66 0.66Fibulin 1C (FIB1C) 0.61 0.64 0.64 Interferon lnducble T Cell AlphaChemoattractant (ITAC) 0.57 0.62 0.62 Growth Hormone (GH) 0.56 0.65 0.65Heparin Binding EGF Like Growth Factor (HBEGF) 0.61 0.62 0.62 NeuronalCell Adhesion Molecule (NrCAM) 0.57 0.63 0.63 Growth Regulated AlphaProtein (GROalpha) 0.58 0.62 0.62 Growth Differentation Factor 15(GDF15) 0.60 0.63 0.63 Mast Stem Cell Growth Factor Receptor (SCFR) 0.470.67 0.67 Cadherin 1 (Ecad) 0.69 0.63 0.69 Angiogenin 0.60 0.64 0.64Sortilin 0.60 0.62 0.62 Alpha 1 Antitrypsin (AAT) 0.61 0.64 0.64Immunoglobulin M (IgM) 0.48 0.64 0.64 Pulmonary and Activation RegulatedChemokine (PARC) 0.60 0.64 0.64 Pulmonary Surfactant Associated ProteinD (SP-D) 0.58 0.63 0.63 B Cell Activating Factor (BAFF) 0.63 0.64 0.64Adrenomedullin (ADM) 0.55 0.67 0.67 Pigment Epithelium Derived Factor(PEDF) 0.60 0.66 0.66 Interleukin 1 Receptor Antagonist (IL1ra) 0.630.62 0.63 Thyrosine Binding Globulin (TBG) 0.57 0.64 0.64 Microalbumin0.59 0.63 0.63 Leptin 0.52 0.65 0.65 Eotaxin 2 0.61 0.64 0.64 Insulinlike Growth Factor Binding Protein 2 (IGFBP2) 0.56 0.63 0.63 Resistin0.52 0.62 0.62 Cathepsin D 0.56 0.65 0.65 E-Selectin 0.60 0.64 0.64YKL40 0.48 0.63 0.63 Interleukin 22 (IL22) 0.56 0.63 0.63Cacinoembryonic Antigen (CEA) 0.58 0.61 0.61 Interleukin 8 (IL8) 0.620.62 0.62 Cancer Antigen 15-3 (CA 15-3) 0.54 0.64 0.64 Leptin Receptor(LeptinR) 0.54 0.66 0.66 Insulin 0.57 0.65 0.65 Monocyte ChemotacticProtein 1 (MCP1) 0.60 0.62 0.62 Prolactin (PRL) 0.48 0.64 0.64Tetranectin 0.53 0.62 0.62 Carcinoembryonic Antigen Related CellAdhesion 0.56 0.64 0.64 Molecule 1 (CEACAM1) 6Ckine 0.55 0.63 0.63 SerumAmyloid P Component (SAP) 0.58 0.63 0.63 Complement Factor H RelatedProtein 1 (CFHR1) 0.63 0.62 0.63 Chemokine CC-4 (HCC-4) 0.57 0.62 0.62Complement C3 (C3) 0.63 0.61 0.63 Alpha Fetoprotein (AFP) 0.59 0.63 0.63Angiopoietin 1 (ANG-1) 0.59 0.63 0.63 Interleukin 18 (IL18) 0.58 0.650.65 Gelsolin 0.57 0.63 0.63 Tenascin C (TN-C) 0.52 0.63 0.63Vitronectin 0.62 0.61 0.62 Beta 2 Microglobulin (B2M) 0.53 0.63 0.63Pancreatic Secretory Trypsin Inhibitor (TATI) 0.54 0.62 0.62 MatrixMetalloproteinase 3 (MMP3) 0.61 0.63 0.63 Omentin 0.53 0.61 0.61Interleukin 18 Binding Protein (IL 18bp) 0.58 0.62 0.62 Apolipoprotein D(ApoD) 0.62 0.62 0.62 Monoctye Chemotactic Protein 4 (MCP-4) 0.53 0.620.62 Apolipoprotein E (Apo-E) 0.48 0.63 0.63 ST2 0.59 0.63 0.63Thrombospondin 1 0.58 0.62 0.62 Gastric Inhibitory Polypeptide (GIP)0.60 0.64 0.64 Matrix Metalloproteinase 7 (MMP7) 0.58 0.62 0.62Intercellular Adhesion Molecule 1 (ICAM-1) 0.60 0.62 0.62 DickkopfRelated Protein 1 (DKK1) 0.51 0.61 0.61

TABLE 13 2 Biomarker Multivariate Analysis. Combination of Vascular CellAdhesion Molecule 1 (VCAM1) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Macrophage Derived Chemokine(MDC) 0.59 0.66 0.66 Vascular Endothelial Growth Factor (VEGF) 0.46 0.660.66 Ficolin 3 0.56 0.66 0.66 Immunoglobulin A (IgA) 0.56 0.66 0.66Factor VII 0.67 0.66 0.67 Interleukin 6 Receptor (IL6R) 0.46 0.66 0.66Receptor for Advanced Glycosylation End Products (RAGE) 0.61 0.69 0.69Fibulin 1C (FIB1C) 0.56 0.63 0.63 Interferon lnducble T Cell AlphaChemoattractant (ITAC) 0.65 0.61 0.65 Growth Hormone (GH) 0.55 0.66 0.66Heparin Binding EGF Like Growth Factor (HBEGF) 0.55 0.62 0.62 NeuronalCell Adhesion Molecule (NrCAM) 0.67 0.65 0.67 Growth Regulated AlphaProtein (GROalpha) 0.67 0.62 0.67 Growth Differentation Factor 15(GDF15) 0.64 0.62 0.64 Mast Stem Cell Growth Factor Receptor (SCFR) 0.500.66 0.66 Cadherin 1 (Ecad) 0.64 0.62 0.64 Angiogenin 0.54 0.63 0.63Sortilin 0.57 0.62 0.62 Alpha 1 Antitrypsin (AAT) 0.50 0.64 0.64Immunoglobulin M (IgM) 0.61 0.62 0.62 Pulmonary and Activation RegulatedChemokine (PARC) 0.58 0.64 0.64 Pulmonary Surfactant Associated ProteinD (SP-D) 0.53 0.60 0.60 B Cell Activating Factor (BAFF) 0.71 0.66 0.71Adrenomedullin (ADM) 0.62 0.67 0.67 Pigment Epithelium Derived Factor(PEDF) 0.63 0.66 0.66 Interleukin 1 Receptor Antagonist (IL1ra) 0.590.66 0.66 Thyrosine Binding Globulin (TBG) 0.52 0.63 0.63 Microalbumin0.64 0.66 0.66 Leptin 0.56 0.68 0.68 Eotaxin 2 0.67 0.62 0.67 Insulinlike Growth Factor Binding Protein 2 (IGFBP2) 0.57 0.62 0.62 Resistin0.56 0.63 0.63 Cathepsin D 0.54 0.64 0.64 E-Selectin 0.57 0.67 0.67YKL40 0.55 0.62 0.62 Interleukin 22 (IL22) 0.50 0.63 0.63Cacinoembryonic Antigen (CEA) 0.57 0.63 0.63 Interleukin 8 (IL8) 0.630.62 0.63 Cancer Antigen 15-3 (CA 15-3) 0.52 0.62 0.62 Leptin Receptor(LeptinR) 0.50 0.65 0.65 Insulin 0.52 0.65 0.65 Monocyte ChemotacticProtein 1 (MCP1) 0.55 0.64 0.64 Prolactin (PRL) 0.53 0.64 0.64Tetranectin 0.52 0.62 0.62 Carcinoembryonic Antigen Related CellAdhesion 0.57 0.65 0.65 Molecule 1 (CEACAM1) 6Ckine 0.56 0.65 0.65 SerumAmyloid P Component (SAP) 0.57 0.64 0.64 Complement Factor H RelatedProtein 1 (CFHR1) 0.50 0.62 0.62 Chemokine CC-4 (HCC-4) 0.58 0.63 0.63Complement C3 (C3) 0.62 0.61 0.62 Alpha Fetoprotein (AFP) 0.46 0.66 0.66Angiopoietin 1 (ANG-1) 0.57 0.62 0.62 Interleukin 18 (IL18) 0.67 0.670.67 Gelsolin 0.59 0.62 0.62 Tenascin C (TN-C) 0.55 0.64 0.64Vitronectin 0.62 0.62 0.62 Beta 2 Microglobulin (B2M) 0.57 0.62 0.62Pancreatic Secretory Trypsin Inhibitor (TATI) 0.55 0.62 0.62 MatrixMetalloproteinase 3 (MMP3) 0.64 0.65 0.65 Omentin 0.59 0.62 0.62Interleukin 18 Binding Protein (IL 18bp) 0.65 0.63 0.65 Apolipoprotein D(ApoD) 0.69 0.63 0.69 Monoctye Chemotactic Protein 4 (MCP-4) 0.49 0.630.63 Apolipoprotein E (Apo-E) 0.47 0.65 0.65 ST2 0.61 0.62 0.62Thrombospondin 1 0.56 0.62 0.62 Gastric Inhibitory Polypeptide (GIP)0.58 0.64 0.64 Matrix Metalloproteinase 7 (MMP7) 0.57 0.64 0.64Intercellular Adhesion Molecule 1 (ICAM-1) 0.55 0.62 0.62 DickkopfRelated Protein 1 (DKK1) 0.53 0.63 0.63

TABLE 14 2 Biomarker Multivariate Analysis. Combination of MacrophageDerived Chemokine (MDC) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Vascular Endothelial GrowthFactor (VEGF) 0.57 0.66 0.66 Ficolin 3 0.52 0.66 0.66 Immunoglobulin A(IgA) 0.50 0.64 0.64 Factor VII 0.58 0.63 0.63 Interleukin 6 Receptor(IL6R) 0.49 0.64 0.64 Receptor for Advanced Glycosylation End Products(RAGE) 0.57 0.66 0.66 Fibulin 1C (FIB1C) 0.54 0.63 0.63 Interferonlnducble T Cell Alpha Chemoattractant (ITAC) 0.62 0.62 0.62 GrowthHormone (GH) 0.56 0.63 0.63 Heparin Binding EGF Like Growth Factor(HBEGF) 0.51 0.63 0.63 Neuronal Cell Adhesion Molecule (NrCAM) 0.62 0.660.66 Growth Regulated Alpha Protein (GROalpha) 0.63 0.62 0.63 GrowthDifferentation Factor 15 (GDF15) 0.57 0.64 0.64 Mast Stem Cell GrowthFactor Receptor (SCFR) 0.53 0.65 0.65 Cadherin 1 (Ecad) 0.59 0.63 0.63Angiogenin 0.57 0.64 0.64 Sortilin 0.57 0.64 0.64 Alpha 1 Antitrypsin(AAT) 0.54 0.65 0.65 Immunoglobulin M (IgM) 0.59 0.64 0.64 Pulmonary andActivation Regulated Chemokine (PARC) 0.57 0.66 0.66 PulmonarySurfactant Associated Protein D (SP-D) 0.54 0.63 0.63 B Cell ActivatingFactor (BAFF) 0.56 0.65 0.65 Adrenomedullin (ADM) 0.62 0.67 0.67 PigmentEpithelium Derived Factor (PEDF) 0.60 0.66 0.66 Interleukin 1 ReceptorAntagonist (IL1ra) 0.54 0.63 0.63 Thyrosine Binding Globulin (TBG) 0.540.63 0.63 Microalbumin 0.57 0.64 0.64 Leptin 0.53 0.64 0.64 Eotaxin 20.61 0.63 0.63 Insulin like Growth Factor Binding Protein 2 (IGFBP2)0.55 0.63 0.63 Resistin 0.53 0.62 0.62 Cathepsin D 0.53 0.65 0.65E-Selectin 0.53 0.66 0.66 YKL40 0.55 0.63 0.63 Interleukin 22 (IL22)0.49 0.62 0.62 Cacinoembryonic Antigen (CEA) 0.57 0.62 0.62 Interleukin8 (IL8) 0.62 0.64 0.64 Cancer Antigen 15-3 (CA 15-3) 0.52 0.63 0.63Leptin Receptor (LeptinR) 0.53 0.63 0.63 Insulin 0.61 0.63 0.63 MonocyteChemotactic Protein 1 (MCP1) 0.55 0.63 0.63 Prolactin (PRL) 0.53 0.650.65 Tetranectin 0.49 0.63 0.63 Carcinoembryonic Antigen Related CellAdhesion 0.57 0.64 0.64 Molecule 1 (CEACAM1) 6Ckine 0.59 0.64 0.64 SerumAmyloid P Component (SAP) 0.65 0.63 0.65 Complement Factor H RelatedProtein 1 (CFHR1) 0.57 0.62 0.62 Chemokine CC-4 (HCC-4) 0.56 0.63 0.63Complement C3 (C3) 0.56 0.63 0.63 Alpha Fetoprotein (AFP) 0.49 0.64 0.64Angiopoietin 1 (ANG-1) 0.60 0.63 0.63 Interleukin 18 (IL18) 0.62 0.660.66 Gelsolin 0.54 0.63 0.63 Tenascin C (TN-C) 0.56 0.64 0.64Vitronectin 0.56 0.62 0.62 Beta 2 Microglobulin (B2M) 0.58 0.63 0.63Pancreatic Secretory Trypsin Inhibitor (TATI) 0.49 0.63 0.63 MatrixMetalloproteinase 3 (MMP3) 0.59 0.64 0.64 Omentin 0.51 0.63 0.63Interleukin 18 Binding Protein (IL 18bp) 0.62 0.64 0.64 Apolipoprotein D(ApoD) 0.62 0.62 0.62 Monoctye Chemotactic Protein 4 (MCP-4) 0.55 0.630.63 Apolipoprotein E (Apo-E) 0.62 0.65 0.65 ST2 0.53 0.63 0.63Thrombospondin 1 0.62 0.62 0.62 Gastric Inhibitory Polypeptide (GIP)0.52 0.64 0.64 Matrix Metalloproteinase 7 (MMP7) 0.61 0.63 0.63Intercellular Adhesion Molecule 1 (ICAM-1) 0.56 0.63 0.63 DickkopfRelated Protein 1 (DKK1) 0.52 0.63 0.63

TABLE 15 2 Biomarker Multivariate Analysis. Combination of VascularEndothelial Growth Factor (VEGF) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC Ficolin 3 0.53 0.650.65 Immunoglobulin A (IgA) 0.53 0.64 0.64 Factor VII 0.61 0.63 0.63Interleukin 6 Receptor (IL6R) 0.52 0.63 0.63 Receptor for AdvancedGlycosylation End Products (RAGE) 0.53 0.66 0.66 Fibulin 1C (FIB1C) 0.560.62 0.62 Interferon Inducble T Cell Alpha Chemoattractant (ITAC) 0.490.60 0.60 Growth Hormone (GH) 0.50 0.64 0.64 Heparin Binding EGF LikeGrowth Factor (HBEGF) 0.53 0.63 0.63 Neuronal Cell Adhesion Molecule(NrCAM) 0.57 0.64 0.64 Growth Regulated Alpha Protein (GROalpha) 0.550.60 0.60 Growth Differentation Factor 15 (GDF15) 0.53 0.62 0.62 MastStem Cell Growth Factor Receptor (SCFR) 0.59 0.64 0.64 Cadherin 1 (Ecad)0.55 0.61 0.61 Angiogenin 0.62 0.62 0.62 Sortilin 0.54 0.62 0.62 Alpha 1Antitrypsin (AAT) 0.50 0.61 0.61 Immunoglobulin M (IgM) 0.51 0.61 0.61Pulmonary and Activation Regulated Chemokine (PARC) 0.53 0.67 0.67Pulmonary Surfactant Associated Protein D (SP-D) 0.52 0.60 0.60 B CellActivating Factor (BAFF) 0.60 0.65 0.65 Adrenomedullin (ADM) 0.57 0.660.66 Pigment Epithelium Derived Factor (PEDF) 0.53 0.67 0.67 Interleukin1 Receptor Antagonist (IL1ra) 0.55 0.61 0.61 Thyrosine Binding Globulin(TBG) 0.52 0.66 0.66 Microalbumin 0.62 0.63 0.63 Leptin 0.59 0.63 0.63Eotaxin 2 0.62 0.63 0.63 Insulin like Growth Factor Binding Protein 2(IGFBP2) 0.52 0.62 0.62 Resistin 0.52 0.62 0.62 Cathepsin D 0.51 0.670.67 E-Selectin 0.54 0.67 0.67 YKL40 0.52 0.61 0.61 Interleukin 22(IL22) 0.57 0.66 0.66 Cacinoembryonic Antigen (CEA) 0.49 0.62 0.62Interleukin 8 (IL8) 0.57 0.60 0.60 Cancer Antigen 15-3 (CA 15-3) 0.590.61 0.61 Leptin Receptor (LeptinR) 0.50 0.62 0.62 Insulin 0.58 0.640.64 Monocyte Chemotactic Protein 1 (MCP1) 0.54 0.64 0.64 Prolactin(PRL) 0.55 0.61 0.61 Tetranectin 0.54 0.60 0.60 Carcinoembryonic AntigenRelated Cell Adhesion 0.51 0.61 0.61 Molecule 1 (CEACAM1) 6Ckine 0.510.65 0.65 Serum Amyloid P Component (SAP) 0.58 0.64 0.64 ComplementFactor H Related Protein 1 (CFHR1) 0.52 0.60 0.60 Chemokine CC-4 (HCC-4)0.48 0.60 0.60 Complement C3 (C3) 0.48 0.60 0.60 Alpha Fetoprotein (AFP)0.57 0.63 0.63 Angiopoietin 1 (ANG-1) 0.51 0.60 0.60 Interleukin 18(IL18) 0.55 0.66 0.66 Gelsolin 0.52 0.61 0.61 Tenascin C (TN-C) 0.520.60 0.60 Vitronectin 0.51 0.62 0.62 Beta 2 Microglobulin (B2M) 0.530.63 0.63 Pancreatic Secretory Trypsin Inhibitor (TATI) 0.58 0.61 0.61Matrix Metalloproteinase 3 (MMP3) 0.52 0.64 0.64 Omentin 0.60 0.60 0.60Interleukin 18 Binding Protein (IL 18bp) 0.58 0.62 0.62 Apolipoprotein D(ApoD) 0.56 0.62 0.62 Monoctye Chemotactic Protein 4 (MCP-4) 0.57 0.610.61 Apolipoprotein E (Apo-E) 0.52 0.64 0.64 ST2 0.53 0.61 0.61Thrombospondin 1 0.56 0.60 0.60 Gastric Inhibitory Polypeptide (GIP)0.55 0.65 0.65 Matrix Metalloproteinase 7 (MMP7) 0.52 0.62 0.62Intercellular Adhesion Molecule 1 (ICAM-1) 0.48 0.61 0.61 DickkopfRelated Protein 1 (DKK1) 0.55 0.61 0.61

TABLE 16 2 Biomarker Multivariate Analysis. Combination of Ficolin 3 andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Immunoglobulin A (IgA) 0.56 0.62 0.62 Factor VII 0.580.62 0.62 Interleukin 6 Receptor (IL6R) 0.53 0.64 0.64 Receptor forAdvanced Glycosylation End Products (RAGE) 0.58 0.64 0.64 Fibulin 1C(FIB1C) 0.59 0.64 0.64 Interferon Inducble T Cell Alpha Chemoattractant(ITAC) 0.51 0.61 0.61 Growth Hormone (GH) 0.53 0.64 0.64 Heparin BindingEGF Like Growth Factor (HBEGF) 0.52 0.61 0.61 Neuronal Cell AdhesionMolecule (NrCAM) 0.54 0.64 0.64 Growth Regulated Alpha Protein(GROalpha) 0.53 0.62 0.62 Growth Differentation Factor 15 (GDF15) 0.500.62 0.62 Mast Stem Cell Growth Factor Receptor (SCFR) 0.55 0.65 0.65Cadherin 1 (Ecad) 0.62 0.61 0.62 Angiogenin 0.54 0.63 0.63 Sortilin 0.500.61 0.61 Alpha 1 Antitrypsin (AAT) 0.51 0.60 0.60 Immunoglobulin M(IgM) 0.57 0.63 0.63 Pulmonary and Activation Regulated Chemokine (PARC)0.57 0.64 0.64 Pulmonary Surfactant Associated Protein D (SP-D) 0.570.61 0.61 B Cell Activating Factor (BAFF) 0.52 0.65 0.65 Adrenomedullin(ADM) 0.56 0.66 0.66 Pigment Epithelium Derived Factor (PEDF) 0.57 0.650.65 Interleukin 1 Receptor Antagonist (IL1ra) 0.56 0.64 0.64 ThyrosineBinding Globulin (TBG) 0.59 0.62 0.62 Microalbumin 0.60 0.62 0.62 Leptin0.51 0.65 0.65 Eotaxin 2 0.54 0.62 0.62 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.57 0.60 0.60 Resistin 0.47 0.62 0.62Cathepsin D 0.57 0.65 0.65 E-Selectin 0.47 0.65 0.65 YKL40 0.52 0.620.62 Interleukin 22 (IL22) 0.55 0.61 0.61 Cacinoembryonic Antigen (CEA)0.49 0.61 0.61 Interleukin 8 (IL8) 0.57 0.62 0.62 Cancer Antigen 15-3(CA 15-3) 0.61 0.61 0.61 Leptin Receptor (LeptinR) 0.55 0.63 0.63Insulin 0.54 0.63 0.63 Monocyte Chemotactic Protein 1 (MCP1) 0.57 0.640.64 Prolactin (PRL) 0.62 0.62 0.62 Tetranectin 0.56 0.61 0.61Carcinoembryonic Antigen Related Cell Adhesion 0.50 0.63 0.63 Molecule 1(CEACAM1) 6Ckine 0.49 0.66 0.66 Serum Amyloid P Component (SAP) 0.530.61 0.61 Complement Factor H Related Protein 1 (CFHR1) 0.54 0.62 0.62Chemokine CC-4 (HCC-4) 0.57 0.62 0.62 Complement C3 (C3) 0.54 0.61 0.61Alpha Fetoprotein (AFP) 0.57 0.65 0.65 Angiopoietin 1 (ANG-1) 0.51 0.630.63 Interleukin 18 (IL18) 0.54 0.66 0.66 Gelsolin 0.40 0.62 0.62Tenascin C (TN-C) 0.50 0.62 0.62 Vitronectin 0.48 0.61 0.61 Beta 2Microglobulin (B2M) 0.49 0.62 0.62 Pancreatic Secretory TrypsinInhibitor (TATI) 0.53 0.61 0.61 Matrix Metalloproteinase 3 (MMP3) 0.650.64 0.65 Omentin 0.59 0.62 0.62 Interleukin 18 Binding Protein (IL18bp) 0.55 0.64 0.64 Apolipoprotein D (ApoD) 0.49 0.61 0.61 MonoctyeChemotactic Protein 4 (MCP-4) 0.58 0.61 0.61 Apolipoprotein E (Apo-E)0.49 0.63 0.63 ST2 0.50 0.61 0.61 Thrombospondin 1 0.58 0.61 0.61Gastric Inhibitory Polypeptide (GIP) 0.56 0.62 0.62 MatrixMetalloproteinase 7 (MMP7) 0.50 0.61 0.61 Intercellular AdhesionMolecule 1 (ICAM-1) 0.50 0.62 0.62 Dickkopf Related Protein 1 (DKK1)0.51 0.61 0.61

TABLE 17 2 Biomarker Multivariate Analysis. Combination ofImmunoglobulin A (IgA) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Factor VII 0.55 0.62 0.62Interleukin 6 Receptor (IL6R) 0.55 0.63 0.63 Receptor for AdvancedGlycosylation End Products (RAGE) 0.47 0.63 0.63 Fibulin 1C (FIB1C) 0.580.62 0.62 Interferon Inducble T Cell Alpha Chemoattractant (ITAC) 0.520.59 0.59 Growth Hormone (GH) 0.61 0.60 0.61 Heparin Binding EGF LikeGrowth Factor (HBEGF) 0.54 0.60 0.60 Neuronal Cell Adhesion Molecule(NrCAM) 0.50 0.63 0.63 Growth Regulated Alpha Protein (GROalpha) 0.500.60 0.60 Growth Differentation Factor 15 (GDF15) 0.51 0.59 0.59 MastStem Cell Growth Factor Receptor (SCFR) 0.56 0.63 0.63 Cadherin 1 (Ecad)0.47 0.59 0.59 Angiogenin 0.47 0.61 0.61 Sortilin 0.56 0.59 0.59 Alpha 1Antitrypsin (AAT) 0.51 0.59 0.59 Immunoglobulin M (IgM) 0.55 0.62 0.62Pulmonary and Activation Regulated Chemokine (PARC) 0.53 0.63 0.63Pulmonary Surfactant Associated Protein D (SP-D) 0.58 0.59 0.59 B CellActivating Factor (BAFF) 0.48 0.61 0.61 Adrenomedullin (ADM) 0.49 0.630.63 Pigment Epithelium Derived Factor (PEDF) 0.52 0.67 0.67 Interleukin1 Receptor Antagonist (IL1ra) 0.57 0.62 0.62 Thyrosine Binding Globulin(TBG) 0.62 0.61 0.62 Microalbumin 0.53 0.62 0.62 Leptin 0.52 0.63 0.63Eotaxin 2 0.56 0.59 0.59 Insulin like Growth Factor Binding Protein 2(IGFBP2) 0.49 0.61 0.61 Resistin 0.56 0.60 0.60 Cathepsin D 0.58 0.640.64 E-Selectin 0.44 0.68 0.68 YKL40 0.54 0.59 0.59 Interleukin 22(IL22) 0.66 0.60 0.66 Cacinoembryonic Antigen (CEA) 0.57 0.62 0.62Interleukin 8 (IL8) 0.54 0.59 0.59 Cancer Antigen 15-3 (CA 15-3) 0.660.60 0.66 Leptin Receptor (LeptinR) 0.54 0.60 0.60 Insulin 0.53 0.630.63 Monocyte Chemotactic Protein 1 (MCP1) 0.56 0.62 0.62 Prolactin(PRL) 0.53 0.61 0.61 Tetranectin 0.63 0.59 0.63 Carcinoembryonic AntigenRelated Cell Adhesion 0.63 0.60 0.63 Molecule 1 (CEACAM1) 6Ckine 0.530.61 0.61 Serum Amyloid P Component (SAP) 0.55 0.60 0.60 ComplementFactor H Related Protein 1 (CFHR1) 0.49 0.58 0.58 Chemokine CC-4 (HCC-4)0.60 0.59 0.60 Complement C3 (C3) 0.53 0.58 0.58 Alpha Fetoprotein (AFP)0.57 0.61 0.61 Angiopoietin 1 (ANG-1) 0.57 0.60 0.60 Interleukin 18(IL18) 0.52 0.64 0.64 Gelsolin 0.53 0.60 0.60 Tenascin C (TN-C) 0.550.58 0.58 Vitronectin 0.53 0.57 0.57 Beta 2 Microglobulin (B2M) 0.480.60 0.60 Pancreatic Secretory Trypsin Inhibitor (TATI) 0.60 0.58 0.60Matrix Metalloproteinase 3 (MMP3) 0.53 0.61 0.61 Omentin 0.62 0.59 0.62Interleukin 18 Binding Protein (IL 18bp) 0.62 0.62 0.62 Apolipoprotein D(ApoD) 0.55 0.58 0.58 Monoctye Chemotactic Protein 4 (MCP-4) 0.65 0.590.65 Apolipoprotein E (Apo-E) 0.56 0.61 0.61 ST2 0.57 0.58 0.58Thrombospondin 1 0.56 0.58 0.58 Gastric Inhibitory Polypeptide (GIP)0.62 0.63 0.63 Matrix Metalloproteinase 7 (MMP7) 0.55 0.59 0.59Intercellular Adhesion Molecule 1 (ICAM-1) 0.58 0.59 0.59 DickkopfRelated Protein 1 (DKK1) 0.51 0.59 0.59

TABLE 18 2 Biomarker Multivariate Analysis. Combination of Factor VIIand Analyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Interleukin 6 Receptor (IL6R) 0.59 0.61 0.61 Receptor forAdvanced Glycosylation End Products (RAGE) 0.65 0.64 0.65 Fibulin 1C(FIB1C) 0.53 0.60 0.60 Interferon Inducble T Cell Alpha Chemoattractant(ITAC) 0.46 0.60 0.60 Growth Hormone (GH) 0.60 0.59 0.60 Heparin BindingEGF Like Growth Factor (HBEGF) 0.55 0.60 0.60 Neuronal Cell AdhesionMolecule (NrCAM) 0.62 0.62 0.62 Growth Regulated Alpha Protein(GROalpha) 0.54 0.60 0.60 Growth Differentation Factor 15 (GDF15) 0.630.59 0.63 Mast Stem Cell Growth Factor Receptor (SCFR) 0.53 0.62 0.62Cadherin 1 (Ecad) 0.74 0.59 0.74 Angiogenin 0.64 0.60 0.64 Sortilin 0.560.59 0.59 Alpha 1 Antitrypsin (AAT) 0.56 0.60 0.60 Immunoglobulin M(IgM) 0.63 0.62 0.63 Pulmonary and Activation Regulated Chemokine (PARC)0.59 0.60 0.60 Pulmonary Surfactant Associated Protein D (SP-D) 0.680.59 0.68 B Cell Activating Factor (BAFF) 0.57 0.63 0.63 Adrenomedullin(ADM) 0.58 0.66 0.66 Pigment Epithelium Derived Factor (PEDF) 0.56 0.650.65 Interleukin 1 Receptor Antagonist (IL1ra) 0.52 0.61 0.61 ThyrosineBinding Globulin (TBG) 0.55 0.59 0.59 Microalbumin 0.65 0.62 0.65 Leptin0.57 0.62 0.62 Eotaxin 2 0.67 0.60 0.67 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.51 0.59 0.59 Resistin 0.55 0.59 0.59Cathepsin D 0.48 0.63 0.63 E-Selectin 0.59 0.65 0.65 YKL40 0.55 0.570.57 Interleukin 22 (IL22) 0.53 0.60 0.60 Cacinoembryonic Antigen (CEA)0.67 0.58 0.67 Interleukin 8 (IL8) 0.60 0.58 0.60 Cancer Antigen 15-3(CA 15-3) 0.54 0.59 0.59 Leptin Receptor (LeptinR) 0.66 0.64 0.66Insulin 0.64 0.60 0.64 Monocyte Chemotactic Protein 1 (MCP1) 0.56 0.610.61 Prolactin (PRL) 0.47 0.58 0.58 Tetranectin 0.57 0.57 0.57Carcinoembryonic Antigen Related Cell Adhesion 0.58 0.64 0.64 Molecule 1(CEACAM1) 6Ckine 0.53 0.62 0.62 Serum Amyloid P Component (SAP) 0.540.60 0.60 Complement Factor H Related Protein 1 (CFHR1) 0.56 0.59 0.59Chemokine CC-4 (HCC-4) 0.58 0.58 0.58 Complement C3 (C3) 0.53 0.58 0.58Alpha Fetoprotein (AFP) 0.57 0.62 0.62 Angiopoietin 1 (ANG-1) 0.56 0.580.58 Interleukin 18 (IL18) 0.64 0.63 0.64 Gelsolin 0.57 0.58 0.58Tenascin C (TN-C) 0.52 0.60 0.60 Vitronectin 0.59 0.58 0.59 Beta 2Microglobulin (B2M) 0.52 0.59 0.59 Pancreatic Secretory TrypsinInhibitor (TATI) 0.53 0.58 0.58 Matrix Metalloproteinase 3 (MMP3) 0.620.60 0.62 Omentin 0.51 0.57 0.57 Interleukin 18 Binding Protein (IL18bp) 0.60 0.60 0.60 Apolipoprotein D (ApoD) 0.65 0.58 0.65 MonoctyeChemotactic Protein 4 (MCP-4) 0.57 0.59 0.59 Apolipoprotein E (Apo-E)0.59 0.63 0.63 ST2 0.62 0.58 0.62 Thrombospondin 1 0.59 0.58 0.59Gastric Inhibitory Polypeptide (GIP) 0.64 0.59 0.64 MatrixMetalloproteinase 7 (MMP7) 0.61 0.61 0.61 Intercellular AdhesionMolecule 1 (ICAM-1) 0.55 0.58 0.58 Dickkopf Related Protein 1 (DKK1)0.58 0.58 0.58

TABLE 19 2 Biomarker Multivariate Analysis. Combination of Interleukin 6Receptor (IL6R) and Analyte 2 AUC by AUC by random logistic MaximumAnalyte 2 forest regression AUC Receptor for Advanced Glycosylation EndProducts (RAGE) 0.56 0.64 0.64 Fibulin 1C (FIB1C) 0.54 0.59 0.59Interferon Inducble T Cell Alpha Chemoattractant (ITAC) 0.55 0.57 0.57Growth Hormone (GH) 0.57 0.62 0.62 Heparin Binding EGF Like GrowthFactor (HBEGF) 0.61 0.60 0.61 Neuronal Cell Adhesion Molecule (NrCAM)0.50 0.64 0.64 Growth Regulated Alpha Protein (GROalpha) 0.54 0.42 0.54Growth Differentation Factor 15 (GDF15) 0.57 0.60 0.60 Mast Stem CellGrowth Factor Receptor (SCFR) 0.59 0.62 0.62 Cadherin 1 (Ecad) 0.56 0.580.58 Angiogenin 0.57 0.61 0.61 Sortilin 0.57 0.61 0.61 Alpha 1Antitrypsin (AAT) 0.54 0.59 0.59 Immunoglobulin M (IgM) 0.51 0.59 0.59Pulmonary and Activation Regulated Chemokine (PARC) 0.66 0.65 0.66Pulmonary Surfactant Associated Protein D (SP-D) 0.52 0.58 0.58 B CellActivating Factor (BAFF) 0.56 0.60 0.60 Adrenomedullin (ADM) 0.52 0.650.65 Pigment Epithelium Derived Factor (PEDF) 0.58 0.69 0.69 Interleukin1 Receptor Antagonist (IL1ra) 0.51 0.66 0.66 Thyrosine Binding Globulin(TBG) 0.55 0.60 0.60 Microalbumin 0.59 0.59 0.59 Leptin 0.54 0.64 0.64Eotaxin 2 0.67 0.58 0.67 Insulin like Growth Factor Binding Protein 2(IGFBP2) 0.51 0.60 0.60 Resistin 0.49 0.61 0.61 Cathepsin D 0.56 0.660.66 E-Selectin 0.54 0.66 0.66 YKL40 0.58 0.58 0.58 Interleukin 22(IL22) 0.52 0.62 0.62 Cacinoembryonic Antigen (CEA) 0.54 0.60 0.60Interleukin 8 (IL8) 0.58 0.58 0.58 Cancer Antigen 15-3 (CA 15-3) 0.600.58 0.60 Leptin Receptor (LeptinR) 0.57 0.59 0.59 Insulin 0.48 0.630.63 Monocyte Chemotactic Protein 1 (MCP1) 0.58 0.62 0.62 Prolactin(PRL) 0.60 0.59 0.60 Tetranectin 0.61 0.59 0.61 Carcinoembryonic AntigenRelated Cell Adhesion 0.58 0.58 0.58 Molecule 1 (CEACAM1) 6Ckine 0.520.63 0.63 Serum Amyloid P Component (SAP) 0.56 0.61 0.61 ComplementFactor H Related Protein 1 (CFHR1) 0.49 0.58 0.58 Chemokine CC-4 (HCC-4)0.56 0.58 0.58 Complement C3 (C3) 0.59 0.59 0.59 Alpha Fetoprotein (AFP)0.49 0.60 0.60 Angiopoietin 1 (ANG-1) 0.49 0.58 0.58 Interleukin 18(IL18) 0.50 0.66 0.66 Gelsolin 0.56 0.60 0.60 Tenascin C (TN-C) 0.490.59 0.59 Vitronectin 0.56 0.59 0.59 Beta 2 Microglobulin (B2M) 0.600.60 0.60 Pancreatic Secretory Trypsin Inhibitor (TATI) 0.55 0.58 0.58Matrix Metalloproteinase 3 (MMP3) 0.58 0.60 0.60 Omentin 0.65 0.58 0.65Interleukin 18 Binding Protein (IL 18bp) 0.50 0.62 0.62 Apolipoprotein D(ApoD) 0.60 0.58 0.60 Monoctye Chemotactic Protein 4 (MCP-4) 0.60 0.580.60 Apolipoprotein E (Apo-E) 0.51 0.62 0.62 ST2 0.53 0.58 0.58Thrombospondin 1 0.51 0.59 0.59 Gastric Inhibitory Polypeptide (GIP)0.50 0.60 0.60 Matrix Metalloproteinase 7 (MMP7) 0.55 0.60 0.60Intercellular Adhesion Molecule 1 (ICAM-1) 0.53 0.60 0.60 DickkopfRelated Protein 1 (DKK1) 0.54 0.62 0.62

TABLE 20 2 Biomarker Multivariate Analysis. Combination of Receptor forAdvanced Glycosylation End Products (RAGE) and Analyte 2 AUC by AUC byrandom logistic Maximum Analyte 2 forest regression AUC Fibulin 1C(FIB1C) 0.50 0.65 0.65 Interferon Inducble T Cell Alpha Chemoattractant(ITAC) 0.61 0.61 0.61 Growth Hormone (GH) 0.58 0.62 0.62 Heparin BindingEGF Like Growth Factor (HBEGF) 0.54 0.63 0.63 Neuronal Cell AdhesionMolecule (NrCAM) 0.60 0.62 0.62 Growth Regulated Alpha Protein(GROalpha) 0.60 0.63 0.63 Growth Differentation Factor 15 (GDF15) 0.460.62 0.62 Mast Stem Cell Growth Factor Receptor (SCFR) 0.53 0.66 0.66Cadherin 1 (Ecad) 0.59 0.63 0.63 Angiogenin 0.55 0.61 0.61 Sortilin 0.580.63 0.63 Alpha 1 Antitrypsin (AAT) 0.51 0.63 0.63 Immunoglobulin M(IgM) 0.58 0.60 0.60 Pulmonary and Activation Regulated Chemokine (PARC)0.52 0.65 0.65 Pulmonary Surfactant Associated Protein D (SP-D) 0.550.61 0.61 B Cell Activating Factor (BAFF) 0.55 0.61 0.61 Adrenomedullin(ADM) 0.57 0.65 0.65 Pigment Epithelium Derived Factor (PEDF) 0.57 0.690.69 Interleukin 1 Receptor Antagonist (IL1ra) 0.49 0.65 0.65 ThyrosineBinding Globulin (TBG) 0.47 0.63 0.63 Microalbumin 0.55 0.61 0.61 Leptin0.53 0.64 0.64 Eotaxin 2 0.63 0.62 0.63 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.59 0.61 0.61 Resistin 0.59 0.62 0.62Cathepsin D 0.52 0.67 0.67 E-Selectin 0.56 0.66 0.66 YKL40 0.54 0.620.62 Interleukin 22 (IL22) 0.55 0.64 0.64 Cacinoembryonic Antigen (CEA)0.50 0.62 0.62 Interleukin 8 (IL8) 0.62 0.62 0.62 Cancer Antigen 15-3(CA 15-3) 0.52 0.63 0.63 Leptin Receptor (LeptinR) 0.55 0.63 0.63Insulin 0.55 0.64 0.64 Monocyte Chemotactic Protein 1 (MCP1) 0.58 0.670.67 Prolactin (PRL) 0.53 0.61 0.61 Tetranectin 0.53 0.61 0.61Carcinoembryonic Antigen Related Cell Adhesion 0.51 0.62 0.62 Molecule 1(CEACAM1) 6Ckine 0.48 0.63 0.63 Serum Amyloid P Component (SAP) 0.580.62 0.62 Complement Factor H Related Protein 1 (CFHR1) 0.51 0.61 0.61Chemokine CC-4 (HCC-4) 0.46 0.61 0.61 Complement C3 (C3) 0.58 0.61 0.61Alpha Fetoprotein (AFP) 0.59 0.65 0.65 Angiopoietin 1 (ANG-1) 0.63 0.600.63 Interleukin 18 (IL18) 0.59 0.65 0.65 Gelsolin 0.57 0.64 0.64Tenascin C (TN-C) 0.49 0.62 0.62 Vitronectin 0.59 0.61 0.61 Beta 2Microglobulin (B2M) 0.64 0.64 0.64 Pancreatic Secretory TrypsinInhibitor (TATI) 0.49 0.62 0.62 Matrix Metalloproteinase 3 (MMP3) 0.650.63 0.65 Omentin 0.53 0.61 0.61 Interleukin 18 Binding Protein (IL18bp) 0.66 0.64 0.66 Apolipoprotein D (ApoD) 0.64 0.62 0.64 MonoctyeChemotactic Protein 4 (MCP-4) 0.50 0.62 0.62 Apolipoprotein E (Apo-E)0.61 0.66 0.66 ST2 0.55 0.61 0.61 Thrombospondin 1 0.60 0.62 0.62Gastric Inhibitory Polypeptide (GIP) 0.56 0.64 0.64 MatrixMetalloproteinase 7 (MMP7) 0.56 0.62 0.62 Intercellular AdhesionMolecule 1 (ICAM-1) 0.48 0.62 0.62 Dickkopf Related Protein 1 (DKK1)0.55 0.63 0.63

TABLE 21 2 Biomarker Multivariate Analysis. Combination of Fibulin 1C(FIB1C) and Analyte 2 AUC by AUC by random logistic Maximum Analyte 2forest regression AUC Interferon Inducble T Cell Alpha Chemoattractant(ITAC) 0.59 0.56 0.59 Growth Hormone (GH) 0.55 0.63 0.63 Heparin BindingEGF Like Growth Factor (HBEGF) 0.52 0.57 0.57 Neuronal Cell AdhesionMolecule (NrCAM) 0.55 0.64 0.64 Growth Regulated Alpha Protein(GROalpha) 0.52 0.58 0.58 Growth Differentation Factor 15 (GDF15) 0.570.58 0.58 Mast Stem Cell Growth Factor Receptor (SCFR) 0.53 0.59 0.59Cadherin 1 (Ecad) 0.54 0.57 0.57 Angiogenin 0.47 0.58 0.58 Sortilin 0.550.58 0.58 Alpha 1 Antitrypsin (AAT) 0.52 0.56 0.56 Immunoglobulin M(IgM) 0.56 0.56 0.56 Pulmonary and Activation Regulated Chemokine (PARC)0.58 0.62 0.62 Pulmonary Surfactant Associated Protein D (SP-D) 0.490.57 0.57 B Cell Activating Factor (BAFF) 0.57 0.61 0.61 Adrenomedullin(ADM) 0.56 0.64 0.64 Pigment Epithelium Derived Factor (PEDF) 0.50 0.650.65 Interleukin 1 Receptor Antagonist (IL1ra) 0.50 0.60 0.60 ThyrosineBinding Globulin (TBG) 0.48 0.61 0.61 Microalbumin 0.59 0.58 0.59 Leptin0.56 0.60 0.60 Eotaxin 2 0.61 0.58 0.61 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.50 0.58 0.58 Resistin 0.48 0.58 0.58Cathepsin D 0.52 0.63 0.63 E-Selectin 0.51 0.64 0.64 YKL40 0.57 0.570.57 Interleukin 22 (IL22) 0.61 0.59 0.61 Cacinoembryonic Antigen (CEA)0.49 0.57 0.57 Interleukin 8 (IL8) 0.53 0.58 0.58 Cancer Antigen 15-3(CA 15-3) 0.52 0.57 0.57 Leptin Receptor (LeptinR) 0.58 0.59 0.59Insulin 0.53 0.66 0.66 Monocyte Chemotactic Protein 1 (MCP1) 0.58 0.600.60 Prolactin (PRL) 0.51 0.62 0.62 Tetranectin 0.48 0.57 0.57Carcinoembryonic Antigen Related Cell Adhesion 0.54 0.58 0.58 Molecule 1(CEACAM1) 6Ckine 0.55 0.62 0.62 Serum Amyloid P Component (SAP) 0.570.61 0.61 Complement Factor H Related Protein 1 (CFHR1) 0.50 0.58 0.58Chemokine CC-4 (HCC-4) 0.53 0.58 0.58 Complement C3 (C3) 0.63 0.44 0.63Alpha Fetoprotein (AFP) 0.57 0.58 0.58 Angiopoietin 1 (ANG-1) 0.57 0.570.57 Interleukin 18 (IL18) 0.47 0.63 0.63 Gelsolin 0.55 0.57 0.57Tenascin C (TN-C) 0.49 0.46 0.49 Vitronectin 0.53 0.56 0.56 Beta 2Microglobulin (B2M) 0.58 0.58 0.58 Pancreatic Secretory TrypsinInhibitor (TATI) 0.53 0.57 0.57 Matrix Metalloproteinase 3 (MMP3) 0.660.57 0.66 Omentin 0.59 0.58 0.59 Interleukin 18 Binding Protein (IL18bp) 0.53 0.61 0.61 Apolipoprotein D (ApoD) 0.57 0.58 0.58 MonoctyeChemotactic Protein 4 (MCP-4) 0.50 0.58 0.58 Apolipoprotein E (Apo-E)0.50 0.61 0.61 ST2 0.58 0.56 0.58 Thrombospondin 1 0.57 0.57 0.57Gastric Inhibitory Polypeptide (GIP) 0.52 0.61 0.61 MatrixMetalloproteinase 7 (MMP7) 0.55 0.61 0.61 Intercellular AdhesionMolecule 1 (ICAM-1) 0.53 0.57 0.57 Dickkopf Related Protein 1 (DKK1)0.50 0.56 0.56

TABLE 22 2 Biomarker Multivariate Analysis. Combination of InterferonInducble T Cell Alpha Chemoattractant (ITAC) and Analyte 2 Analyte 2 AUCby random forest AUC by logistic regression Maximum AUC Growth Hormone(GH) 0.55 0.59 0.59 Heparin Binding EGF Like Growth Factor (HBEGF) 0.500.54 0.54 Neuronal Cell Adhesion Molecule (NrCAM) 0.61 0.56 0.61 GrowthRegulated Alpha Protein (GROalpha) 0.44 0.53 0.53 Growth DifferentationFactor 15 (GDF15) 0.61 0.51 0.61 Mast Stem Cell Growth Factor Receptor(SCFR) 0.54 0.60 0.60 Cadherin 1 (Ecad) 0.54 0.50 0.54 Angiogenin 0.530.57 0.57 Sortilin 0.53 0.47 0.53 Alpha 1 Antitrypsin (AAT) 0.59 0.550.59 Immunoglobulin M (IgM) 0.56 0.47 0.56 Pulmonary and ActivationRegulated Chemokine (PARC) 0.61 0.59 0.61 Pulmonary SurfactantAssociated Protein D (SP-D) 0.57 0.55 0.57 B Cell Activating Factor(BAFF) 0.58 0.59 0.59 Adrenomedullin (ADM) 0.60 0.61 0.61 PigmentEpithelium Derived Factor (PEDF) 0.67 0.65 0.67 Interleukin 1 ReceptorAntagonist (IL1ra) 0.50 0.59 0.59 Thyrosine Binding Globulin (TBG) 0.540.60 0.60 Microalbumin 0.65 0.46 0.65 Leptin 0.47 0.60 0.60 Eotaxin 20.58 0.54 0.58 Insulin like Growth Factor Binding Protein 2 (IGFBP2)0.56 0.56 0.56 Resistin 0.53 0.54 0.54 Cathepsin D 0.56 0.63 0.63E-Selectin 0.60 0.63 0.63 YKL40 0.58 0.49 0.58 Interleukin 22 (IL22)0.55 0.56 0.56 Cacinoembryonic Antigen (CEA) 0.51 0.54 0.54 Interleukin8 (IL8) 0.45 0.56 0.56 Cancer Antigen 15-3 (CA 15-3) 0.49 0.53 0.53Leptin Receptor (LeptinR) 0.51 0.58 0.58 Insulin 0.48 0.60 0.60 MonocyteChemotactic Protein 1 (MCP1) 0.58 0.58 0.58 Prolactin (PRL) 0.51 0.540.54 Tetranectin 0.55 0.51 0.55 Carcinoembryonic Antigen Related CellAdhesion 0.52 0.56 0.56 Molecule 1 (CEACAM1) 6Ckine 0.55 0.60 0.60 SerumAmyloid P Component (SAP) 0.54 0.59 0.59 Complement Factor H RelatedProtein 1 (CFHR1) 0.50 0.56 0.56 Chemokine CC-4 (HCC-4) 0.58 0.53 0.58Complement C3 (C3) 0.60 0.53 0.60 Alpha Fetoprotein (AFP) 0.44 0.53 0.53Angiopoietin 1 (ANG-1) 0.51 0.51 0.51 Interleukin 18 (IL18) 0.55 0.630.63 Gelsolin 0.61 0.54 0.61 Tenascin C (TN-C) 0.59 0.53 0.59Vitronectin 0.51 0.49 0.51 Beta 2 Microglobulin (B2M) 0.58 0.55 0.58Pancreatic Secretory Trypsin Inhibitor (TATI) 0.57 0.57 0.57 MatrixMetalloproteinase 3 (MMP3) 0.60 0.57 0.60 Omentin 0.58 0.50 0.58Interleukin 18 Binding Protein (IL 18bp) 0.62 0.58 0.62 Apolipoprotein D(ApoD) 0.60 0.56 0.60 Monoctye Chemotactic Protein 4 (MCP-4) 0.52 0.490.52 Apolipoprotein E (Apo-E) 0.61 0.60 0.61 ST2 0.49 0.55 0.55Thrombospondin 1 0.54 0.52 0.54 Gastric Inhibitory Polypeptide (GIP)0.51 0.58 0.58 Matrix Metalloproteinase 7 (MMP7) 0.63 0.57 0.63Intercellular Adhesion Molecule 1 (ICAM-1) 0.54 0.50 0.54 DickkopfRelated Protein 1 (DKK1) 0.51 0.50 0.51

TABLE 23 2 Biomarker Multivariate Analysis. Combination of GrowthHormone (GH) and Analyte 2 AUC by AUC by random logistic Maximum Analyte2 forest regression AUC Heparin Binding EGF Like Growth Factor (HBEGF)0.52 0.59 0.59 Neuronal Cell Adhesion Molecule (NrCAM) 0.52 0.65 0.65Growth Regulated Alpha Protein (GROalpha) 0.56 0.57 0.57 GrowthDifferentation Factor 15 (GDF15) 0.56 0.65 0.65 Mast Stem Cell GrowthFactor Receptor (SCFR) 0.58 0.62 0.62 Cadherin 1 (Ecad) 0.53 0.60 0.60Angiogenin 0.52 0.61 0.61 Sortilin 0.52 0.57 0.57 Alpha 1 Antitrypsin(AAT) 0.48 0.57 0.57 Immunoglobulin M (IgM) 0.58 0.59 0.59 Pulmonary andActivation Regulated Chemokine (PARC) 0.58 0.65 0.65 PulmonarySurfactant Associated Protein D (SP-D) 0.57 0.61 0.61 B Cell ActivatingFactor (BAFF) 0.50 0.61 0.61 Adrenomedullin (ADM) 0.55 0.64 0.64 PigmentEpithelium Derived Factor (PEDF) 0.52 0.65 0.65 Interleukin 1 ReceptorAntagonist (IL1ra) 0.54 0.59 0.59 Thyrosine Binding Globulin (TBG) 0.580.61 0.61 Microalbumin 0.56 0.58 0.58 Leptin 0.56 0.62 0.62 Eotaxin 20.50 0.60 0.60 Insulin like Growth Factor Binding Protein 2 (IGFBP2)0.54 0.62 0.62 Resistin 0.56 0.59 0.59 Cathepsin D 0.58 0.65 0.65E-Selectin 0.49 0.64 0.64 YKL40 0.50 0.60 0.60 Interleukin 22 (IL22)0.63 0.62 0.63 Cacinoembryonic Antigen (CEA) 0.61 0.63 0.63 Interleukin8 (IL8) 0.54 0.61 0.61 Cancer Antigen 15-3 (CA 15-3) 0.61 0.61 0.61Leptin Receptor (LeptinR) 0.54 0.60 0.60 Insulin 0.57 0.62 0.62 MonocyteChemotactic Protein 1 (MCP1) 0.54 0.62 0.62 Prolactin (PRL) 0.59 0.610.61 Tetranectin 0.47 0.62 0.62 Carcinoembryonic Antigen Related CellAdhesion 0.50 0.60 0.60 Molecule 1 (CEACAM1) 6Ckine 0.54 0.60 0.60 SerumAmyloid P Component (SAP) 0.54 0.61 0.61 Complement Factor H RelatedProtein 1 (CFHR1) 0.56 0.60 0.60 Chemokine CC-4 (HCC-4) 0.54 0.59 0.59Complement C3 (C3) 0.58 0.60 0.60 Alpha Fetoprotein (AFP) 0.49 0.63 0.63Angiopoietin 1 (ANG-1) 0.57 0.61 0.61 Interleukin 18 (IL18) 0.50 0.640.64 Gelsolin 0.61 0.64 0.64 Tenascin C (TN-C) 0.55 0.60 0.60Vitronectin 0.52 0.58 0.58 Beta 2 Microglobulin (B2M) 0.52 0.59 0.59Pancreatic Secretory Trypsin Inhibitor (TATI) 0.50 0.58 0.58 MatrixMetalloproteinase 3 (MMP3) 0.51 0.60 0.60 Omentin 0.62 0.59 0.62Interleukin 18 Binding Protein (IL 18bp) 0.54 0.64 0.64 Apolipoprotein D(ApoD) 0.58 0.60 0.60 Monoctye Chemotactic Protein 4 (MCP-4) 0.53 0.610.61 Apolipoprotein E (Apo-E) 0.55 0.61 0.61 ST2 0.55 0.60 0.60Thrombospondin 1 0.52 0.59 0.59 Gastric Inhibitory Polypeptide (GIP)0.60 0.61 0.61 Matrix Metalloproteinase 7 (MMP7) 0.52 0.57 0.57Intercellular Adhesion Molecule 1 (ICAM-1) 0.55 0.57 0.57 DickkopfRelated Protein 1 (DKK1) 0.55 0.59 0.59

TABLE 24 2 Biomarker Multivariate Analysis. Combination of HeparinBinding EGF Like Growth Factor (HBEGF) and Analyte 2 AUC by random AUCby logistic Maximum Analyte 2 forest regression AUC Neuronal CellAdhesion Molecule (NrCAM) 0.58 0.59 0.59 Growth Regulated Alpha Protein(GROalpha) 0.48 0.58 0.58 Growth Differentation Factor 15 (GDF15) 0.630.57 0.63 Mast Stem Cell Growth Factor Receptor (SCFR) 0.55 0.61 0.61Cadherin 1 (Ecad) 0.59 0.56 0.59 Angiogenin 0.46 0.57 0.57 Sortilin 0.480.53 0.53 Alpha 1 Antitrypsin (AAT) 0.53 0.56 0.56 Immunoglobulin M(IgM) 0.55 0.56 0.56 Pulmonary and Activation Regulated Chemokine (PARC)0.56 0.59 0.59 Pulmonary Surfactant Associated Protein D (SP-D) 0.550.54 0.55 B Cell Activating Factor (BAFF) 0.56 0.61 0.61 Adrenomedullin(ADM) 0.49 0.63 0.63 Pigment Epithelium Derived Factor (PEDF) 0.49 0.650.65 Interleukin 1 Receptor Antagonist (IL1ra) 0.55 0.60 0.60 ThyrosineBinding Globulin (TBG) 0.47 0.60 0.60 Microalbumin 0.51 0.59 0.59 Leptin0.55 0.60 0.60 Eotaxin 2 0.52 0.56 0.56 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.52 0.58 0.58 Resistin 0.49 0.54 0.54Cathepsin D 0.54 0.62 0.62 E-Selectin 0.51 0.63 0.63 YKL40 0.50 0.530.53 Interleukin 22 (IL22) 0.63 0.58 0.63 Cacinoembryonic Antigen (CEA)0.51 0.57 0.57 Interleukin 8 (IL8) 0.62 0.54 0.62 Cancer Antigen 15-3(CA 15-3) 0.56 0.59 0.59 Leptin Receptor (LeptinR) 0.50 0.60 0.60Insulin 0.53 0.59 0.59 Monocyte Chemotactic Protein 1 (MCP1) 0.51 0.580.58 Prolactin (PRL) 0.60 0.57 0.60 Tetranectin 0.54 0.53 0.54Carcinoembryonic Antigen Related Cell Adhesion 0.59 0.60 0.60 Molecule 1(CEACAM1) 6Ckine 0.53 0.60 0.60 Serum Amyloid P Component (SAP) 0.510.58 0.58 Complement Factor H Related Protein 1 (CFHR1) 0.49 0.53 0.53Chemokine CC-4 (HCC-4) 0.48 0.52 0.52 Complement C3 (C3) 0.51 0.53 0.53Alpha Fetoprotein (AFP) 0.50 0.59 0.59 Angiopoietin 1 (ANG-1) 0.46 0.580.58 Interleukin 18 (IL18) 0.64 0.65 0.65 Gelsolin 0.49 0.56 0.56Tenascin C (TN-C) 0.53 0.55 0.55 Vitronectin 0.50 0.53 0.53 Beta 2Microglobulin (B2M) 0.48 0.57 0.57 Pancreatic Secretory TrypsinInhibitor (TATI) 0.51 0.53 0.53 Matrix Metalloproteinase 3 (MMP3) 0.550.55 0.55 Omentin 0.53 0.54 0.54 Interleukin 18 Binding Protein (IL18bp) 0.51 0.59 0.59 Apolipoprotein D (ApoD) 0.59 0.57 0.59 MonoctyeChemotactic Protein 4 (MCP-4) 0.48 0.54 0.54 Apolipoprotein E (Apo-E)0.59 0.61 0.61 ST2 0.56 0.53 0.56 Thrombospondin 1 0.53 0.53 0.53Gastric Inhibitory Polypeptide (GIP) 0.53 0.59 0.59 MatrixMetalloproteinase 7 (MMP7) 0.56 0.57 0.57 Intercellular AdhesionMolecule 1 (ICAM-1) 0.54 0.54 0.54 Dickkopf Related Protein 1 (DKK1)0.57 0.53 0.57

TABLE 25 2 Biomarker Multivariate Analysis. Combination of Neuronal CellAdhesion Molecule (NrCAM) and Analyte 2 AUC by random AUC by logisticMaximum Analyte 2 forest regression AUC Growth Regulated Alpha Protein(GROalpha) 0.48 0.59 0.59 Growth Differentation Factor 15 (GDF15) 0.590.62 0.62 Mast Stem Cell Growth Factor Receptor (SCFR) 0.51 0.64 0.64Cadherin 1 (Ecad) 0.56 0.63 0.63 Angiogenin 0.56 0.62 0.62 Sortilin 0.560.61 0.61 Alpha 1 Antitrypsin (AAT) 0.58 0.64 0.64 Immunoglobulin M(IgM) 0.50 0.59 0.59 Pulmonary and Activation Regulated Chemokine (PARC)0.56 0.64 0.64 Pulmonary Surfactant Associated Protein D (SP-D) 0.500.62 0.62 B Cell Activating Factor (BAFF) 0.58 0.61 0.61 Adrenomedullin(ADM) 0.61 0.63 0.63 Pigment Epithelium Derived Factor (PEDF) 0.61 0.690.69 Interleukin 1 Receptor Antagonist (IL1ra) 0.57 0.63 0.63 ThyrosineBinding Globulin (TBG) 0.63 0.60 0.63 Microalbumin 0.58 0.62 0.62 Leptin0.56 0.64 0.64 Eotaxin 2 0.67 0.61 0.67 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.53 0.59 0.59 Resistin 0.45 0.62 0.62Cathepsin D 0.54 0.65 0.65 E-Selectin 0.63 0.69 0.69 YKL40 0.60 0.600.60 Interleukin 22 (IL22) 0.51 0.62 0.62 Cacinoembryonic Antigen (CEA)0.53 0.64 0.64 Interleukin 8 (IL8) 0.60 0.63 0.63 Cancer Antigen 15-3(CA 15-3) 0.57 0.62 0.62 Leptin Receptor (LeptinR) 0.58 0.63 0.63Insulin 0.58 0.63 0.63 Monocyte Chemotactic Protein 1 (MCP1) 0.62 0.660.66 Prolactin (PRL) 0.52 0.62 0.62 Tetranectin 0.54 0.59 0.59Carcinoembryonic Antigen Related Cell Adhesion 0.54 0.61 0.61 Molecule 1(CEACAM1) 6Ckine 0.62 0.61 0.62 Serum Amyloid P Component (SAP) 0.460.61 0.61 Complement Factor H Related Protein 1 (CFHR1) 0.54 0.60 0.60Chemokine CC-4 (HCC-4) 0.53 0.59 0.59 Complement C3 (C3) 0.63 0.59 0.63Alpha Fetoprotein (AFP) 0.56 0.63 0.63 Angiopoietin 1 (ANG-1) 0.57 0.610.61 Interleukin 18 (IL18) 0.58 0.66 0.66 Gelsolin 0.58 0.60 0.60Tenascin C (TN-C) 0.53 0.60 0.60 Vitronectin 0.63 0.57 0.63 Beta 2Microglobulin (B2M) 0.62 0.62 0.62 Pancreatic Secretory TrypsinInhibitor (TATI) 0.55 0.60 0.60 Matrix Metalloproteinase 3 (MMP3) 0.620.63 0.63 Omentin 0.52 0.60 0.60 Interleukin 18 Binding Protein (IL18bp) 0.56 0.64 0.64 Apolipoprotein D (ApoD) 0.59 0.61 0.61 MonoctyeChemotactic Protein 4 (MCP-4) 0.56 0.60 0.60 Apolipoprotein E (Apo-E)0.59 0.61 0.61 ST2 0.58 0.61 0.61 Thrombospondin 1 0.55 0.60 0.60Gastric Inhibitory Polypeptide (GIP) 0.55 0.63 0.63 MatrixMetalloproteinase 7 (MMP7) 0.55 0.62 0.62 Intercellular AdhesionMolecule 1 (ICAM-1) 0.62 0.60 0.62 Dickkopf Related Protein 1 (DKK1)0.52 0.60 0.60

TABLE 26 2 Biomarker Multivariate Analysis. Combination of GrowthRegulated Alpha Protein (GROalpha) and Analyte 2 AUC by random AUC bylogistic Maximum Analyte 2 forest regression AUC Growth DifferentationFactor 15 (GDF15) 0.60 0.56 0.60 Mast Stem Cell Growth Factor Receptor(SCFR) 0.54 0.58 0.58 Cadherin 1 (Ecad) 0.63 0.56 0.63 Angiogenin 0.520.58 0.58 Sortilin 0.49 0.57 0.57 Alpha 1 Antitrypsin (AAT) 0.56 0.540.56 Immunoglobulin M (IgM) 0.60 0.53 0.60 Pulmonary and ActivationRegulated Chemokine (PARC) 0.58 0.62 0.62 Pulmonary SurfactantAssociated Protein D (SP-D) 0.54 0.53 0.54 B Cell Activating Factor(BAFF) 0.57 0.59 0.59 Adrenomedullin (ADM) 0.53 0.64 0.64 PigmentEpithelium Derived Factor (PEDF) 0.66 0.67 0.67 Interleukin 1 ReceptorAntagonist (IL1ra) 0.46 0.60 0.60 Thyrosine Binding Globulin (TBG) 0.610.61 0.61 Microalbumin 0.62 0.56 0.62 Leptin 0.59 0.62 0.62 Eotaxin 20.64 0.58 0.64 Insulin like Growth Factor Binding Protein 2 (IGFBP2)0.49 0.57 0.57 Resistin 0.52 0.54 0.54 Cathepsin D 0.55 0.62 0.62E-Selectin 0.54 0.64 0.64 YKL40 0.48 0.56 0.56 Interleukin 22 (IL22)0.50 0.57 0.57 Cacinoembryonic Antigen (CEA) 0.49 0.55 0.55 Interleukin8 (IL8) 0.59 0.54 0.59 Cancer Antigen 15-3 (CA 15-3) 0.56 0.53 0.56Leptin Receptor (LeptinR) 0.56 0.61 0.61 Insulin 0.45 0.62 0.62 MonocyteChemotactic Protein 1 (MCP1) 0.59 0.60 0.60 Prolactin (PRL) 0.53 0.540.54 Tetranectin 0.50 0.46 0.50 Carcinoembryonic Antigen Related CellAdhesion 0.45 0.56 0.56 Molecule 1 (CEACAM1) 6Ckine 0.55 0.63 0.63 SerumAmyloid P Component (SAP) 0.59 0.61 0.61 Complement Factor H RelatedProtein 1 (CFHR1) 0.51 0.56 0.56 Chemokine CC-4 (HCC-4) 0.52 0.55 0.55Complement C3 (C3) 0.60 0.56 0.60 Alpha Fetoprotein (AFP) 0.52 0.56 0.56Angiopoietin 1 (ANG-1) 0.50 0.54 0.54 Interleukin 18 (IL18) 0.65 0.630.65 Gelsolin 0.45 0.55 0.55 Tenascin C (TN-C) 0.53 0.54 0.54Vitronectin 0.65 0.46 0.65 Beta 2 Microglobulin (B2M) 0.59 0.55 0.59Pancreatic Secretory Trypsin Inhibitor (TATI) 0.55 0.47 0.55 MatrixMetalloproteinase 3 (MMP3) 0.60 0.56 0.60 Omentin 0.54 0.48 0.54Interleukin 18 Binding Protein (IL 18bp) 0.66 0.58 0.66 Apolipoprotein D(ApoD) 0.60 0.54 0.60 Monoctye Chemotactic Protein 4 (MCP-4) 0.50 0.550.55 Apolipoprotein E (Apo-E) 0.58 0.60 0.60 ST2 0.63 0.47 0.63Thrombospondin 1 0.53 0.54 0.54 Gastric Inhibitory Polypeptide (GIP)0.50 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.62 0.58 0.62Intercellular Adhesion Molecule 1 (ICAM-1) 0.55 0.52 0.55 DickkopfRelated Protein 1 (DKK1) 0.52 0.53 0.53

TABLE 27 2 Biomarker Multivariate Analysis. Combination of GrowthDifferentation Factor 15 (GDF15) and Analyte 2 AUC by random AUC bylogistic Maximum Analyte 2 forest regression AUC Mast Stem Cell GrowthFactor Receptor (SCFR) 0.52 0.61 0.61 Cadherin 1 (Ecad) 0.71 0.54 0.71Angiogenin 0.53 0.59 0.59 Sortilin 0.58 0.54 0.58 Alpha 1 Antitrypsin(AAT) 0.64 0.56 0.64 Immunoglobulin M (IgM) 0.61 0.54 0.61 Pulmonary andActivation Regulated Chemokine (PARC) 0.63 0.59 0.63 PulmonarySurfactant Associated Protein D (SP-D) 0.52 0.54 0.54 B Cell ActivatingFactor (BAFF) 0.56 0.58 0.58 Adrenomedullin (ADM) 0.57 0.63 0.63 PigmentEpithelium Derived Factor (PEDF) 0.62 0.65 0.65 Interleukin 1 ReceptorAntagonist (IL1ra) 0.57 0.59 0.59 Thyrosine Binding Globulin (TBG) 0.610.60 0.61 Microalbumin 0.65 0.45 0.65 Leptin 0.62 0.61 0.62 Eotaxin 20.64 0.58 0.64 Insulin like Growth Factor Binding Protein 2 (IGFBP2)0.66 0.57 0.66 Resistin 0.55 0.57 0.57 Cathepsin D 0.49 0.62 0.62E-Selectin 0.53 0.64 0.64 YKL40 0.48 0.54 0.54 Interleukin 22 (IL22)0.59 0.58 0.59 Cacinoembryonic Antigen (CEA) 0.54 0.57 0.57 Interleukin8 (IL8) 0.58 0.46 0.58 Cancer Antigen 15-3 (CA 15-3) 0.64 0.54 0.64Leptin Receptor (LeptinR) 0.57 0.60 0.60 Insulin 0.52 0.64 0.64 MonocyteChemotactic Protein 1 (MCP1) 0.64 0.59 0.64 Prolactin (PRL) 0.52 0.560.56 Tetranectin 0.59 0.54 0.59 Carcinoembryonic Antigen Related CellAdhesion 0.59 0.57 0.59 Molecule 1 (CEACAM1) 6Ckine 0.55 0.60 0.60 SerumAmyloid P Component (SAP) 0.56 0.59 0.59 Complement Factor H RelatedProtein 1 (CFHR1) 0.57 0.47 0.57 Chemokine CC-4 (HCC-4) 0.58 0.55 0.58Complement C3 (C3) 0.69 0.54 0.69 Alpha Fetoprotein (AFP) 0.52 0.61 0.61Angiopoietin 1 (ANG-1) 0.59 0.56 0.59 Interleukin 18 (IL18) 0.60 0.620.62 Gelsolin 0.52 0.55 0.55 Tenascin C (TN-C) 0.47 0.55 0.55Vitronectin 0.47 0.54 0.54 Beta 2 Microglobulin (B2M) 0.56 0.55 0.56Pancreatic Secretory Trypsin Inhibitor (TATI) 0.49 0.46 0.49 MatrixMetalloproteinase 3 (MMP3) 0.61 0.55 0.61 Omentin 0.50 0.56 0.56Interleukin 18 Binding Protein (IL 18bp) 0.54 0.58 0.58 Apolipoprotein D(ApoD) 0.62 0.56 0.62 Monoctye Chemotactic Protein 4 (MCP-4) 0.53 0.570.57 Apolipoprotein E (Apo-E) 0.59 0.60 0.60 ST2 0.59 0.55 0.59Thrombospondin 1 0.56 0.52 0.56 Gastric Inhibitory Polypeptide (GIP)0.55 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.60 0.57 0.60Intercellular Adhesion Molecule 1 (ICAM-1) 0.53 0.54 0.54 DickkopfRelated Protein 1 (DKK1) 0.49 0.55 0.55

TABLE 28 2 Biomarker Multivariate Analysis. Combination of Mast StemCell Growth Factor Receptor (SCFR) and Analyte 2 AUC by random AUC bylogistic Maximum Analyte 2 forest regression AUC Cadherin 1 (Ecad) 0.590.60 0.60 Angiogenin 0.57 0.62 0.62 Sortilin 0.49 0.60 0.60 Alpha 1Antitrypsin (AAT) 0.51 0.61 0.61 Immunoglobulin M (IgM) 0.59 0.59 0.59Pulmonary and Activation Regulated Chemokine (PARC) 0.50 0.66 0.66Pulmonary Surfactant Associated Protein D (SP-D) 0.57 0.59 0.59 B CellActivating Factor (BAFF) 0.53 0.63 0.63 Adrenomedullin (ADM) 0.53 0.680.68 Pigment Epithelium Derived Factor (PEDF) 0.50 0.70 0.70 Interleukin1 Receptor Antagonist (IL1ra) 0.50 0.67 0.67 Thyrosine Binding Globulin(TBG) 0.58 0.63 0.63 Microalbumin 0.63 0.62 0.63 Leptin 0.52 0.68 0.68Eotaxin 2 0.57 0.62 0.62 Insulin like Growth Factor Binding Protein 2(IGFBP2) 0.56 0.62 0.62 Resistin 0.59 0.61 0.61 Cathepsin D 0.49 0.670.67 E-Selectin 0.57 0.67 0.67 YKL40 0.53 0.60 0.60 Interleukin 22(IL22) 0.50 0.62 0.62 Cacinoembryonic Antigen (CEA) 0.58 0.59 0.59Interleukin 8 (IL8) 0.60 0.59 0.60 Cancer Antigen 15-3 (CA 15-3) 0.570.59 0.59 Leptin Receptor (LeptinR) 0.52 0.62 0.62 Insulin 0.52 0.680.68 Monocyte Chemotactic Protein 1 (MCP1) 0.56 0.62 0.62 Prolactin(PRL) 0.63 0.61 0.63 Tetranectin 0.54 0.59 0.59 Carcinoembryonic AntigenRelated Cell Adhesion 0.50 0.63 0.63 Molecule 1 (CEACAM1) 6Ckine 0.610.65 0.65 Serum Amyloid P Component (SAP) 0.52 0.66 0.66 ComplementFactor H Related Protein 1 (CFHR1) 0.49 0.60 0.60 Chemokine CC-4 (HCC-4)0.51 0.60 0.60 Complement C3 (C3) 0.53 0.63 0.63 Alpha Fetoprotein (AFP)0.61 0.62 0.62 Angiopoietin 1 (ANG-1) 0.52 0.60 0.60 Interleukin 18(IL18) 0.53 0.68 0.68 Gelsolin 0.54 0.60 0.60 Tenascin C (TN-C) 0.460.60 0.60 Vitronectin 0.52 0.61 0.61 Beta 2 Microglobulin (B2M) 0.480.62 0.62 Pancreatic Secretory Trypsin Inhibitor (TATI) 0.59 0.60 0.60Matrix Metalloproteinase 3 (MMP3) 0.60 0.61 0.61 Omentin 0.69 0.60 0.69Interleukin 18 Binding Protein (IL 18bp) 0.52 0.66 0.66 Apolipoprotein D(ApoD) 0.59 0.62 0.62 Monoctye Chemotactic Protein 4 (MCP-4) 0.63 0.600.63 Apolipoprotein E (Apo-E) 0.48 0.65 0.65 ST2 0.53 0.60 0.60Thrombospondin 1 0.53 0.59 0.59 Gastric Inhibitory Polypeptide (GIP)0.54 0.65 0.65 Matrix Metalloproteinase 7 (MMP7) 0.54 0.61 0.61Intercellular Adhesion Molecule 1 (ICAM-1) 0.57 0.59 0.59 DickkopfRelated Protein 1 (DKK1) 0.56 0.61 0.61

TABLE 29 2 Biomarker Multivariate Analysis. Combination of Cadherin 1(Ecad) and Analyte 2 AUC by random AUC by logistic Maximum Analyte 2forest regression AUC Angiogenin 0.50 0.58 0.58 Sortilin 0.54 0.55 0.55Alpha 1 Antitrypsin (AAT) 0.44 0.54 0.54 Immunoglobulin M (IgM) 0.600.48 0.60 Pulmonary and Activation Regulated Chemokine (PARC) 0.64 0.600.64 Pulmonary Surfactant Associated Protein D (SP-D) 0.56 0.51 0.56 BCell Activating Factor (BAFF) 0.60 0.60 0.60 Adrenomedullin (ADM) 0.590.63 0.63 Pigment Epithelium Derived Factor (PEDF) 0.68 0.64 0.68Interleukin 1 Receptor Antagonist (IL1ra) 0.58 0.60 0.60 ThyrosineBinding Globulin (TBG) 0.54 0.60 0.60 Microalbumin 0.59 0.55 0.59 Leptin0.61 0.61 0.61 Eotaxin 2 0.65 0.57 0.65 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.51 0.57 0.57 Resistin 0.59 0.57 0.59Cathepsin D 0.59 0.62 0.62 E-Selectin 0.49 0.63 0.63 YKL40 0.58 0.550.58 Interleukin 22 (IL22) 0.59 0.56 0.59 Cacinoembryonic Antigen (CEA)0.64 0.54 0.64 Interleukin 8 (IL8) 0.67 0.53 0.67 Cancer Antigen 15-3(CA 15-3) 0.52 0.46 0.52 Leptin Receptor (LeptinR) 0.62 0.58 0.62Insulin 0.58 0.63 0.63 Monocyte Chemotactic Protein 1 (MCP1) 0.70 0.590.70 Prolactin (PRL) 0.53 0.55 0.55 Tetranectin 0.57 0.54 0.57Carcinoembryonic Antigen Related Cell Adhesion 0.55 0.57 0.57 Molecule 1(CEACAM1) 6Ckine 0.55 0.62 0.62 Serum Amyloid P Component (SAP) 0.580.57 0.58 Complement Factor H Related Protein 1 (CFHR1) 0.54 0.55 0.55Chemokine CC-4 (HCC-4) 0.57 0.53 0.57 Complement C3 (C3) 0.66 0.52 0.66Alpha Fetoprotein (AFP) 0.54 0.58 0.58 Angiopoietin 1 (ANG-1) 0.63 0.560.63 Interleukin 18 (IL18) 0.58 0.63 0.63 Gelsolin 0.60 0.54 0.60Tenascin C (TN-C) 0.56 0.54 0.56 Vitronectin 0.65 0.47 0.65 Beta 2Microglobulin (B2M) 0.55 0.56 0.56 Pancreatic Secretory TrypsinInhibitor (TATI) 0.58 0.54 0.58 Matrix Metalloproteinase 3 (MMP3) 0.700.55 0.70 Omentin 0.56 0.57 0.57 Interleukin 18 Binding Protein (IL18bp) 0.54 0.58 0.58 Apolipoprotein D (ApoD) 0.60 0.57 0.60 MonoctyeChemotactic Protein 4 (MCP-4) 0.56 0.57 0.57 Apolipoprotein E (Apo-E)0.54 0.60 0.60 ST2 0.60 0.57 0.60 Thrombospondin 1 0.61 0.53 0.61Gastric Inhibitory Polypeptide (GIP) 0.53 0.60 0.60 MatrixMetalloproteinase 7 (MMP7) 0.54 0.55 0.55 Intercellular AdhesionMolecule 1 (ICAM-1) 0.56 0.53 0.56 Dickkopf Related Protein 1 (DKK1)0.58 0.48 0.58

TABLE 30 2 Biomarker Multivariate Analysis. Combination of Angiogeninand Analyte 2 AUC by random AUC by logistic Maximum Analyte 2 forestregression AUC Sortilin 0.53 0.58 0.58 Alpha 1 Antitrypsin (AAT) 0.520.58 0.58 Immunoglobulin M (IgM) 0.56 0.58 0.58 Pulmonary and ActivationRegulated Chemokine (PARC) 0.62 0.63 0.63 Pulmonary SurfactantAssociated Protein D (SP-D) 0.53 0.58 0.58 B Cell Activating Factor(BAFF) 0.52 0.59 0.59 Adrenomedullin (ADM) 0.52 0.62 0.62 PigmentEpithelium Derived Factor (PEDF) 0.62 0.65 0.65 Interleukin 1 ReceptorAntagonist (IL1ra) 0.50 0.61 0.61 Thyrosine Binding Globulin (TBG) 0.470.60 0.60 Microalbumin 0.57 0.59 0.59 Leptin 0.64 0.62 0.64 Eotaxin 20.60 0.60 0.60 Insulin like Growth Factor Binding Protein 2 (IGFBP2)0.56 0.57 0.57 Resistin 0.53 0.59 0.59 Cathepsin D 0.54 0.62 0.62E-Selectin 0.63 0.64 0.64 YKL40 0.57 0.58 0.58 Interleukin 22 (IL22)0.44 0.60 0.60 Cacinoembryonic Antigen (CEA) 0.50 0.59 0.59 Interleukin8 (IL8) 0.52 0.58 0.58 Cancer Antigen 15-3 (CA 15-3) 0.61 0.58 0.61Leptin Receptor (LeptinR) 0.50 0.61 0.61 Insulin 0.55 0.64 0.64 MonocyteChemotactic Protein 1 (MCP1) 0.47 0.60 0.60 Prolactin (PRL) 0.54 0.570.57 Tetranectin 0.54 0.58 0.58 Carcinoembryonic Antigen Related CellAdhesion 0.56 0.59 0.59 Molecule 1 (CEACAM1) 6Ckine 0.50 0.62 0.62 SerumAmyloid P Component (SAP) 0.57 0.59 0.59 Complement Factor H RelatedProtein 1 (CFHR1) 0.53 0.59 0.59 Chemokine CC-4 (HCC-4) 0.57 0.58 0.58Complement C3 (C3) 0.61 0.55 0.61 Alpha Fetoprotein (AFP) 0.50 0.63 0.63Angiopoietin 1 (ANG-1) 0.57 0.57 0.57 Interleukin 18 (IL18) 0.59 0.640.64 Gelsolin 0.52 0.58 0.58 Tenascin C (TN-C) 0.52 0.58 0.58Vitronectin 0.50 0.56 0.56 Beta 2 Microglobulin (B2M) 0.57 0.58 0.58Pancreatic Secretory Trypsin Inhibitor (TATI) 0.53 0.58 0.58 MatrixMetalloproteinase 3 (MMP3) 0.59 0.58 0.59 Omentin 0.58 0.58 0.58Interleukin 18 Binding Protein (IL 18bp) 0.54 0.60 0.60 Apolipoprotein D(ApoD) 0.64 0.57 0.64 Monoctye Chemotactic Protein 4 (MCP-4) 0.57 0.580.58 Apolipoprotein E (Apo-E) 0.50 0.63 0.63 ST2 0.51 0.58 0.58Thrombospondin 1 0.50 0.58 0.58 Gastric Inhibitory Polypeptide (GIP)0.62 0.61 0.62 Matrix Metalloproteinase 7 (MMP7) 0.53 0.58 0.58Intercellular Adhesion Molecule 1 (ICAM-1) 0.52 0.57 0.57 DickkopfRelated Protein 1 (DKK1) 0.55 0.57 0.57

TABLE 31 2 Biomarker Multivariate Analysis. Combination of Sortilin andAnalyte 2 AUC by random AUC by logistic Maximum Analyte 2 forestregression AUC Alpha 1 Antitrypsin (AAT) 0.60 0.55 0.60 Immunoglobulin M(IgM) 0.56 0.45 0.56 Pulmonary and Activation Regulated Chemokine (PARC)0.53 0.59 0.59 Pulmonary Surfactant Associated Protein D (SP-D) 0.550.53 0.55 B Cell Activating Factor (BAFF) 0.50 0.58 0.58 Adrenomedullin(ADM) 0.60 0.64 0.64 Pigment Epithelium Derived Factor (PEDF) 0.60 0.650.65 Interleukin 1 Receptor Antagonist (IL1ra) 0.47 0.58 0.58 ThyrosineBinding Globulin (TBG) 0.48 0.59 0.59 Microalbumin 0.58 0.58 0.58 Leptin0.51 0.59 0.59 Eotaxin 2 0.62 0.57 0.62 Insulin like Growth FactorBinding Protein 2 (IGFBP2) 0.55 0.58 0.58 Resistin 0.51 0.58 0.58Cathepsin D 0.51 0.62 0.62 E-Selectin 0.49 0.64 0.64 YKL40 0.52 0.540.54 Interleukin 22 (IL22) 0.56 0.58 0.58 Cacinoembryonic Antigen (CEA)0.54 0.56 0.56 Interleukin 8 (IL8) 0.56 0.53 0.56 Cancer Antigen 15-3(CA 15-3) 0.47 0.60 0.60 Leptin Receptor (LeptinR) 0.54 0.62 0.62Insulin 0.52 0.61 0.61 Monocyte Chemotactic Protein 1 (MCP1) 0.60 0.580.60 Prolactin (PRL) 0.47 0.57 0.57 Tetranectin 0.43 0.53 0.53Carcinoembryonic Antigen Related Cell Adhesion 0.49 0.59 0.59 Molecule 1(CEACAM1) 6Ckine 0.55 0.60 0.60 Serum Amyloid P Component (SAP) 0.500.58 0.58 Complement Factor H Related Protein 1 (CFHR1) 0.58 0.53 0.58Chemokine CC-4 (HCC-4) 0.54 0.54 0.54 Complement C3 (C3) 0.52 0.54 0.54Alpha Fetoprotein (AFP) 0.55 0.61 0.61 Angiopoietin 1 (ANG-1) 0.50 0.570.57 Interleukin 18 (IL18) 0.55 0.63 0.63 Gelsolin 0.56 0.47 0.56Tenascin C (TN-C) 0.48 0.53 0.53 Vitronectin 0.53 0.53 0.53 Beta 2Microglobulin (B2M) 0.52 0.56 0.56 Pancreatic Secretory TrypsinInhibitor (TATI) 0.55 0.53 0.55 Matrix Metalloproteinase 3 (MMP3) 0.570.56 0.57 Omentin 0.59 0.55 0.59 Interleukin 18 Binding Protein (IL18bp) 0.55 0.58 0.58 Apolipoprotein D (ApoD) 0.60 0.58 0.60 MonoctyeChemotactic Protein 4 (MCP-4) 0.57 0.53 0.57 Apolipoprotein E (Apo-E)0.52 0.60 0.60 ST2 0.52 0.54 0.54 Thrombospondin 1 0.57 0.53 0.57Gastric Inhibitory Polypeptide (GIP) 0.49 0.58 0.58 MatrixMetalloproteinase 7 (MMP7) 0.51 0.57 0.57 Intercellular AdhesionMolecule 1 (ICAM-1) 0.54 0.54 0.54 Dickkopf Related Protein 1 (DKK1)0.59 0.47 0.59

TABLE 32 2 Biomarker Multivariate Analysis. Combination of Alpha 1Antitrypsin (AAT) and Analyte 2 AUC by AUC by random logistic MaximumAnalyte 2 forest regression AUC Immunoglobulin M (IgM) 0.50 0.57 0.57Pulmonary and Activation Regulated 0.62 0.63 0.63 Chemokine (PARC)Pulmonary Surfactant Associated 0.50 0.45 0.50 Protein D (SP-D) B CellActivating Factor (BAFF) 0.60 0.61 0.61 Adrenomedullin (ADM) 0.55 0.650.65 Pigment Epithelium Derived 0.64 0.66 0.66 Factor (PEDF) Interleukin1 Receptor Antagonist 0.49 0.65 0.65 (IL1ra) Thyrosine Binding Globulin(TBG) 0.55 0.63 0.63 Microalbumin 0.59 0.59 0.59 Leptin 0.54 0.64 0.64Eotaxin 2 0.58 0.59 0.59 Insulin like Growth Factor Binding 0.57 0.600.60 Protein 2 (IGFBP2) Resistin 0.58 0.58 0.58 Cathepsin D 0.54 0.650.65 E-Selectin 0.51 0.65 0.65 YKL40 0.54 0.54 0.54 Interleukin 22(IL22) 0.49 0.56 0.56 Cacinoembryonic Antigen (CEA) 0.51 0.57 0.57Interleukin 8 (IL8) 0.56 0.55 0.56 Cancer Antigen 15-3 (CA 15-3) 0.560.57 0.57 Leptin Receptor (LeptinR) 0.50 0.59 0.59 Insulin 0.52 0.580.58 Monocyte Chemotactic Protein 1 0.51 0.60 0.60 (MCP1) Prolactin(PRL) 0.53 0.57 0.57 Tetranectin 0.51 0.56 0.56 Carcinoembryonic Antigen0.55 0.59 0.59 Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.520.63 0.63 Serum Amyloid P Component (SAP) 0.57 0.62 0.62 ComplementFactor H Related 0.50 0.55 0.55 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4)0.57 0.55 0.57 Complement C3 (C3) 0.53 0.58 0.58 Alpha Fetoprotein (AFP)0.54 0.58 0.58 Angiopoietin 1 (ANG-1) 0.59 0.56 0.59 Interleukin 18(IL18) 0.63 0.62 0.63 Gelsolin 0.67 0.56 0.67 Tenascin C (TN-C) 0.510.57 0.57 Vitronectin 0.50 0.56 0.56 Beta 2 Microglobulin (B2M) 0.570.44 0.57 Pancreatic Secretory Trypsin 0.54 0.56 0.56 Inhibitor (TATI)Matrix Metalloproteinase 3 (MMP3) 0.60 0.56 0.60 Omentin 0.54 0.56 0.56Interleukin 18 Binding Protein 0.49 0.58 0.58 (IL 18bp) Apolipoprotein D(ApoD) 0.60 0.57 0.60 Monoctye Chemotactic Protein 4 0.52 0.55 0.55(MCP-4) Apolipoprotein E (Apo-E) 0.59 0.62 0.62 ST2 0.51 0.55 0.55Thrombospondin 1 0.65 0.54 0.65 Gastric Inhibitory Polypeptide (GIP)0.56 0.58 0.58 Matrix Metalloproteinase 7 (MMP7) 0.62 0.59 0.62Intercellular Adhesion Molecule 1 0.58 0.57 0.58 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.49 0.54 0.54

TABLE 33 2 Biomarker Multivariate Analysis. Combination ofImmunoglobulin M (IgM) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Pulmonary and ActivationRegulated 0.64 0.60 0.64 Chemokine (PARC) Pulmonary SurfactantAssociated 0.55 0.46 0.55 Protein D (SP-D) B Cell Activating Factor(BAFF) 0.60 0.58 0.60 Adrenomedullin (ADM) 0.56 0.63 0.63 PigmentEpithelium Derived 0.56 0.66 0.66 Factor (PEDF) Interleukin 1 ReceptorAntagonist 0.48 0.62 0.62 (IL1ra) Thyrosine Binding Globulin (TBG) 0.540.59 0.59 Microalbumin 0.67 0.57 0.67 Leptin 0.57 0.64 0.64 Eotaxin 20.60 0.56 0.60 Insulin like Growth Factor Binding 0.69 0.57 0.69 Protein2 (IGFBP2) Resistin 0.56 0.58 0.58 Cathepsin D 0.51 0.63 0.63 E-Selectin0.55 0.63 0.63 YKL40 0.48 0.54 0.54 Interleukin 22 (IL22) 0.52 0.58 0.58Cacinoembryonic Antigen (CEA) 0.53 0.56 0.56 Interleukin 8 (IL8) 0.550.54 0.55 Cancer Antigen 15-3 (CA 15-3) 0.57 0.56 0.57 Leptin Receptor(LeptinR) 0.58 0.58 0.58 Insulin 0.67 0.65 0.67 Monocyte ChemotacticProtein 1 0.59 0.61 0.61 (MCP1) Prolactin (PRL) 0.52 0.56 0.56Tetranectin 0.51 0.53 0.53 Carcinoembryonic Antigen 0.56 0.58 0.58Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.60 0.63 0.63 SerumAmyloid P Component (SAP) 0.65 0.61 0.65 Complement Factor H Related0.51 0.54 0.54 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.55 0.55 0.55Complement C3 (C3) 0.66 0.56 0.66 Alpha Fetoprotein (AFP) 0.58 0.57 0.58Angiopoietin 1 (ANG-1) 0.62 0.54 0.62 Interleukin 18 (IL18) 0.60 0.660.66 Gelsolin 0.68 0.46 0.68 Tenascin C (TN-C) 0.62 0.54 0.62Vitronectin 0.53 0.54 0.54 Beta 2 Microglobulin (B2M) 0.60 0.56 0.60Pancreatic Secretory Trypsin 0.56 0.47 0.56 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.64 0.55 0.64 Omentin 0.54 0.47 0.54Interleukin 18 Binding Protein 0.62 0.58 0.62 (IL 18bp) Apolipoprotein D(ApoD) 0.70 0.59 0.70 Monoctye Chemotactic Protein 4 0.52 0.54 0.54(MCP-4) Apolipoprotein E (Apo-E) 0.60 0.62 0.62 ST2 0.46 0.54 0.54Thrombospondin 1 0.52 0.55 0.55 Gastric Inhibitory Polypeptide (GIP)0.56 0.60 0.60 Matrix Metalloproteinase 7 (MMP7) 0.58 0.58 0.58Intercellular Adhesion Molecule 1 0.56 0.54 0.56 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.55 0.55 0.55

TABLE 34 2 Biomarker Multivariate Analysis. Combination of Pulmonary andActivation Regulated Chemokine (PARC) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC Pulmonary SurfactantAssociated 0.52 0.60 0.60 Protein D (SP-D) B Cell Activating Factor(BAFF) 0.63 0.62 0.63 Adrenomedullin (ADM) 0.55 0.63 0.63 PigmentEpithelium Derived Factor 0.54 0.67 0.67 (PEDF) Interleukin 1 ReceptorAntagonist 0.58 0.62 0.62 (IL1ra) Thyrosine Binding Globulin (TBG) 0.590.62 0.62 Microalbumin 0.60 0.61 0.61 Leptin 0.52 0.63 0.63 Eotaxin 20.64 0.61 0.64 Insulin like Growth Factor Binding 0.59 0.61 0.61 Protein2 (IGFBP2) Resistin 0.53 0.61 0.61 Cathepsin D 0.52 0.62 0.62 E-Selectin0.50 0.65 0.65 YKL40 0.61 0.59 0.61 Interleukin 22 (IL22) 0.49 0.62 0.62Cacinoembryonic Antigen (CEA) 0.55 0.61 0.61 Interleukin 8 (IL8) 0.570.59 0.59 Cancer Antigen 15-3 (CA 15-3) 0.48 0.61 0.61 Leptin Receptor(LeptinR) 0.57 0.62 0.62 Insulin 0.59 0.64 0.64 Monocyte ChemotacticProtein 1 0.55 0.62 0.62 (MCP1) Prolactin (PRL) 0.55 0.62 0.62Tetranectin 0.48 0.60 0.60 Carcinoembryonic Antigen 0.53 0.62 0.62Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.50 0.63 0.63 SerumAmyloid P Component (SAP) 0.53 0.62 0.62 Complement Factor H Related0.64 0.42 0.64 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.57 0.59 0.59Complement C3 (C3) 0.62 0.60 0.62 Alpha Fetoprotein (AFP) 0.57 0.63 0.63Angiopoietin 1 (ANG-1) 0.58 0.61 0.61 Interleukin 18 (IL18) 0.60 0.650.65 Gelsolin 0.60 0.61 0.61 Tenascin C (TN-C) 0.51 0.61 0.61Vitronectin 0.54 0.60 0.60 Beta 2 Microglobulin (B2M) 0.60 0.60 0.60Pancreatic Secretory Trypsin 0.56 0.59 0.59 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.64 0.62 0.64 Omentin 0.51 0.59 0.59Interleukin 18 Binding Protein 0.54 0.61 0.61 (IL 18bp) Apolipoprotein D(ApoD) 0.70 0.60 0.70 Monoctye Chemotactic Protein 4 0.53 0.59 0.59(MCP-4) Apolipoprotein E (Apo-E) 0.57 0.64 0.64 ST2 0.60 0.59 0.60Thrombospondin 1 0.60 0.59 0.60 Gastric Inhibitory Polypeptide (GIP)0.58 0.63 0.63 Matrix Metalloproteinase 7 (MMP7) 0.59 0.61 0.61Intercellular Adhesion Molecule 1 0.47 0.59 0.59 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.55 0.59 0.59

TABLE 35 2 Biomarker Multivariate Analysis. Combination of PulmonarySurfactant Associated Protein D (SP-D) and Analyte 2 AUC by AUC byrandom logistic Maximum Analyte 2 forest regression AUC B CellActivating Factor (BAFF) 0.55 0.58 0.58 Adrenomedullin (ADM) 0.52 0.620.62 Pigment Epithelium Derived 0.64 0.65 0.65 Factor (PEDF) Interleukin1 Receptor Antagonist 0.52 0.59 0.59 (IL1ra) Thyrosine Binding Globulin(TBG) 0.63 0.59 0.63 Microalbumin 0.58 0.45 0.58 Leptin 0.50 0.61 0.61Eotaxin 2 0.62 0.55 0.62 Insulin like Growth Factor Binding 0.50 0.560.56 Protein 2 (IGFBP2) Resistin 0.55 0.56 0.56 Cathepsin D 0.53 0.620.62 E-Selectin 0.50 0.64 0.64 YKL40 0.58 0.51 0.58 Interleukin 22(IL22) 0.54 0.56 0.56 Cacinoembryonic Antigen (CEA) 0.56 0.54 0.56Interleukin 8 (IL8) 0.55 0.50 0.55 Cancer Antigen 15-3 (CA 15-3) 0.570.55 0.57 Leptin Receptor (LeptinR) 0.55 0.57 0.57 Insulin 0.50 0.620.62 Monocyte Chemotactic Protein 1 0.55 0.59 0.59 (MCP1) Prolactin(PRL) 0.52 0.55 0.55 Tetranectin 0.56 0.51 0.56 Carcinoembryonic Antigen0.55 0.57 0.57 Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.510.61 0.61 Serum Amyloid P Component (SAP) 0.53 0.57 0.57 ComplementFactor H Related 0.53 0.50 0.53 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4)0.50 0.52 0.52 Complement C3 (C3) 0.46 0.52 0.52 Alpha Fetoprotein (AFP)0.58 0.55 0.58 Angiopoietin 1 (ANG-1) 0.53 0.54 0.54 Interleukin 18(IL18) 0.56 0.65 0.65 Gelsolin 0.53 0.47 0.53 Tenascin C (TN-C) 0.540.54 0.54 Vitronectin 0.60 0.50 0.60 Beta 2 Microglobulin (B2M) 0.500.56 0.56 Pancreatic Secretory Trypsin 0.52 0.52 0.52 Inhibitor (TATI)Matrix Metalloproteinase 3 (MMP3) 0.54 0.55 0.55 Omentin 0.70 0.51 0.70Interleukin 18 Binding Protein 0.50 0.59 0.59 (IL 18bp) Apolipoprotein D(ApoD) 0.54 0.55 0.55 Monoctye Chemotactic Protein 4 0.55 0.53 0.55(MCP-4) Apolipoprotein E (Apo-E) 0.48 0.60 0.60 ST2 0.50 0.52 0.52Thrombospondin 1 0.56 0.51 0.56 Gastric Inhibitory Polypeptide (GIP)0.54 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.50 0.56 0.56Intercellular Adhesion Molecule 1 0.63 0.52 0.63 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.59 0.53 0.59

TABLE 36 2 Biomarker Multivariate Analysis. Combination of B CellActivating Factor (BAFF) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Adrenomedullin (ADM) 0.58 0.640.64 Pigment Epithelium Derived Factor 0.54 0.67 0.67 (PEDF) Interleukin1 Receptor Antagonist 0.47 0.62 0.62 (IL1ra) Thyrosine Binding Globulin(TBG) 0.49 0.59 0.59 Microalbumin 0.61 0.57 0.61 Leptin 0.46 0.61 0.61Eotaxin 2 0.64 0.59 0.64 Insulin like Growth Factor Binding 0.50 0.600.60 Protein 2 (IGFBP2) Resistin 0.51 0.62 0.62 Cathepsin D 0.60 0.640.64 E-Selectin 0.50 0.66 0.66 YKL40 0.60 0.58 0.60 Interleukin 22(IL22) 0.55 0.60 0.60 Cacinoembryonic Antigen (CEA) 0.57 0.60 0.60Interleukin 8 (IL8) 0.61 0.59 0.61 Cancer Antigen 15-3 (CA 15-3) 0.510.59 0.59 Leptin Receptor (LeptinR) 0.50 0.60 0.60 Insulin 0.58 0.650.65 Monocyte Chemotactic Protein 1 0.50 0.62 0.62 (MCP1) Prolactin(PRL) 0.53 0.59 0.59 Tetranectin 0.56 0.59 0.59 Carcinoembryonic Antigen0.48 0.57 0.57 Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.530.63 0.63 Serum Amyloid P Component (SAP) 0.55 0.61 0.61 ComplementFactor H Related 0.54 0.58 0.58 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4)0.60 0.58 0.60 Complement C3 (C3) 0.59 0.59 0.59 Alpha Fetoprotein (AFP)0.50 0.63 0.63 Angiopoietin 1 (ANG-1) 0.57 0.59 0.59 Interleukin 18(IL18) 0.47 0.65 0.65 Gelsolin 0.57 0.62 0.62 Tenascin C (TN-C) 0.550.60 0.60 Vitronectin 0.59 0.58 0.59 Beta 2 Microglobulin (B2M) 0.490.61 0.61 Pancreatic Secretory Trypsin 0.56 0.58 0.58 Inhibitor (TATI)Matrix Metalloproteinase 3 (MMP3) 0.61 0.61 0.61 Omentin 0.57 0.58 0.58Interleukin 18 Binding Protein 0.60 0.61 0.61 (IL 18bp) Apolipoprotein D(ApoD) 0.63 0.58 0.63 Monoctye Chemotactic Protein 4 0.44 0.59 0.59(MCP-4) Apolipoprotein E (Apo-E) 0.56 0.64 0.64 ST2 0.54 0.58 0.58Thrombospondin 1 0.58 0.58 0.58 Gastric Inhibitory Polypeptide (GIP)0.50 0.63 0.63 Matrix Metalloproteinase 7 (MMP7) 0.57 0.59 0.59Intercellular Adhesion Molecule 1 0.53 0.58 0.58 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.53 0.59 0.59

TABLE 37 2 Biomarker Multivariate Analysis. Combination ofAdrenomedullin (ADM) and Analyte 2 AUC by AUC by random logistic MaximumAnalyte 2 forest regression AUC Pigment Epithelium Derived 0.57 0.660.66 Factor (PEDF) Interleukin 1 Receptor Antagonist 0.51 0.63 0.63(IL1ra) Thyrosine Binding Globulin (TBG) 0.58 0.63 0.63 Microalbumin0.63 0.63 0.63 Leptin 0.51 0.62 0.62 Eotaxin 2 0.61 0.63 0.63 Insulinlike Growth Factor Binding 0.42 0.61 0.61 Protein 2 (IGFBP2) Resistin0.48 0.63 0.63 Cathepsin D 0.50 0.65 0.65 E-Selectin 0.55 0.70 0.70YKL40 0.56 0.62 0.62 Interleukin 22 (IL22) 0.48 0.63 0.63Cacinoembryonic Antigen (CEA) 0.64 0.64 0.64 Interleukin 8 (IL8) 0.610.62 0.62 Cancer Antigen 15-3 (CA 15-3) 0.48 0.63 0.63 Leptin Receptor(LeptinR) 0.55 0.63 0.63 Insulin 0.51 0.64 0.64 Monocyte ChemotacticProtein 1 0.56 0.66 0.66 (MCP1) Prolactin (PRL) 0.55 0.62 0.62Tetranectin 0.55 0.63 0.63 Carcinoembryonic Antigen 0.56 0.61 0.61Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.51 0.65 0.65 SerumAmyloid P Component (SAP) 0.47 0.61 0.61 Complement Factor H Related0.49 0.63 0.63 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.52 0.61 0.61Complement C3 (C3) 0.55 0.62 0.62 Alpha Fetoprotein (AFP) 0.54 0.65 0.65Angiopoietin 1 (ANG-1) 0.51 0.62 0.62 Interleukin 18 (IL18) 0.55 0.660.66 Gelsolin 0.57 0.63 0.63 Tenascin C (TN-C) 0.46 0.62 0.62Vitronectin 0.58 0.62 0.62 Beta 2 Microglobulin (B2M) 0.59 0.62 0.62Pancreatic Secretory Trypsin 0.47 0.63 0.63 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.65 0.64 0.65 Omentin 0.54 0.63 0.63Interleukin 18 Binding Protein 0.60 0.65 0.65 (IL 18bp) Apolipoprotein D(ApoD) 0.57 0.62 0.62 Monoctye Chemotactic Protein 4 0.55 0.62 0.62(MCP-4) Apolipoprotein E (Apo-E) 0.54 0.64 0.64 ST2 0.53 0.62 0.62Thrombospondin 1 0.52 0.62 0.62 Gastric Inhibitory Polypeptide (GIP)0.58 0.63 0.63 Matrix Metalloproteinase 7 (MMP7) 0.51 0.64 0.64Intercellular Adhesion 0.54 0.63 0.63 Molecule 1 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.48 0.62 0.62

TABLE 38 2 Biomarker Multivariate Analysis. Combination of PigmentEpithelium Derived Factor (PEDF) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC Interleukin 1 ReceptorAntagonist 0.53 0.64 0.64 (IL1ra) Thyrosine Binding Globulin (TBG) 0.510.64 0.64 Microalbumin 0.66 0.66 0.66 Leptin 0.53 0.64 0.64 Eotaxin 20.69 0.64 0.69 Insulin like Growth Factor Binding 0.47 0.64 0.64 Protein2 (IGFBP2) Resistin 0.53 0.64 0.64 Cathepsin D 0.56 0.67 0.67 E-Selectin0.51 0.67 0.67 YKL40 0.56 0.65 0.65 Interleukin 22 (IL22) 0.57 0.65 0.65Cacinoembryonic Antigen (CEA) 0.52 0.65 0.65 Interleukin 8 (IL8) 0.450.64 0.64 Cancer Antigen 15-3 (CA 15-3) 0.60 0.66 0.66 Leptin Receptor(LeptinR) 0.53 0.65 0.65 Insulin 0.55 0.64 0.64 Monocyte ChemotacticProtein 1 0.61 0.66 0.66 (MCP1) Prolactin (PRL) 0.56 0.64 0.64Tetranectin 0.56 0.65 0.65 Carcinoembryonic Antigen 0.55 0.67 0.67Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.56 0.66 0.66 SerumAmyloid P Component (SAP) 0.51 0.64 0.64 Complement Factor H Related0.55 0.65 0.65 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.58 0.64 0.64Complement C3 (C3) 0.60 0.65 0.65 Alpha Fetoprotein (AFP) 0.52 0.68 0.68Angiopoietin 1 (ANG-1) 0.60 0.64 0.64 Interleukin 18 (IL18) 0.51 0.660.66 Gelsolin 0.58 0.65 0.65 Tenascin C (TN-C) 0.55 0.66 0.66Vitronectin 0.50 0.65 0.65 Beta 2 Microglobulin (B2M) 0.60 0.65 0.65Pancreatic Secretory Trypsin 0.64 0.64 0.64 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.54 0.65 0.65 Omentin 0.53 0.64 0.64Interleukin 18 Binding Protein 0.59 0.65 0.65 (IL 18bp) Apolipoprotein D(ApoD) 0.60 0.64 0.64 Monoctye Chemotactic Protein 4 0.49 0.65 0.65(MCP-4) Apolipoprotein E (Apo-E) 0.53 0.64 0.64 ST2 0.56 0.64 0.64Thrombospondin 1 0.55 0.64 0.64 Gastric Inhibitory Polypeptide (GIP)0.55 0.65 0.65 Matrix Metalloproteinase 7 (MMP7) 0.59 0.65 0.65Intercellular Adhesion 0.51 0.64 0.64 Molecule 1 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.50 0.65 0.65

TABLE 39 2 Biomarker Multivariate Analysis. Combination of Interleukin 1Receptor Antagonist (IL1ra) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Thyrosine Binding Globulin (TBG)0.55 0.61 0.61 Microalbumin 0.56 0.62 0.62 Leptin 0.56 0.61 0.61 Eotaxin2 0.59 0.60 0.60 Insulin like Growth Factor Binding 0.54 0.61 0.61Protein 2 (IGFBP2) Resistin 0.54 0.58 0.58 Cathepsin D 0.59 0.64 0.64E-Selectin 0.53 0.67 0.67 YKL40 0.53 0.59 0.59 Interleukin 22 (IL22)0.52 0.62 0.62 Cacinoembryonic Antigen (CEA) 0.53 0.62 0.62 Interleukin8 (IL8) 0.56 0.58 0.58 Cancer Antigen 15-3 (CA 15-3) 0.59 0.61 0.61Leptin Receptor (LeptinR) 0.51 0.61 0.61 Insulin 0.55 0.61 0.61 MonocyteChemotactic Protein 1 0.52 0.63 0.63 (MCP1) Prolactin (PRL) 0.55 0.610.61 Tetranectin 0.56 0.59 0.59 Carcinoembryonic Antigen 0.50 0.63 0.63Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.54 0.60 0.60 SerumAmyloid P Component (SAP) 0.51 0.60 0.60 Complement Factor H Related0.61 0.58 0.61 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.57 0.58 0.58Complement C3 (C3) 0.58 0.58 0.58 Alpha Fetoprotein (AFP) 0.49 0.62 0.62Angiopoietin 1 (ANG-1) 0.53 0.59 0.59 Interleukin 18 (IL18) 0.53 0.620.62 Gelsolin 0.62 0.59 0.62 Tenascin C (TN-C) 0.52 0.59 0.59Vitronectin 0.55 0.59 0.59 Beta 2 Microglobulin (B2M) 0.49 0.59 0.59Pancreatic Secretory Trypsin 0.51 0.58 0.58 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.56 0.59 0.59 Omentin 0.63 0.59 0.63Interleukin 18 Binding Protein 0.49 0.59 0.59 (IL 18bp) Apolipoprotein D(ApoD) 0.68 0.59 0.68 Monoctye Chemotactic Protein 4 0.53 0.59 0.59(MCP-4) Apolipoprotein E (Apo-E) 0.53 0.62 0.62 ST2 0.55 0.58 0.58Thrombospondin 1 0.51 0.59 0.59 Gastric Inhibitory Polypeptide (GIP)0.58 0.62 0.62 Matrix Metalloproteinase 7 (MMP7) 0.57 0.62 0.62Intercellular Adhesion Molecule 1 0.50 0.58 0.58 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.54 0.59 0.59

TABLE 40 2 Biomarker Multivariate Analysis. Combination of ThyrosineBinding Globulin (TBG) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Microalbumin 0.48 0.59 0.59Leptin 0.60 0.60 0.60 Eotaxin 2 0.55 0.61 0.61 Insulin like GrowthFactor Binding 0.59 0.58 0.59 Protein 2 (IGFBP2) Resistin 0.49 0.58 0.58Cathepsin D 0.56 0.63 0.63 E-Selectin 0.50 0.65 0.65 YKL40 0.54 0.580.58 Interleukin 22 (IL22) 0.58 0.60 0.60 Cacinoembryonic Antigen (CEA)0.54 0.62 0.62 Interleukin 8 (IL8) 0.47 0.58 0.58 Cancer Antigen 15-3(CA 15-3) 0.60 0.59 0.60 Leptin Receptor (LeptinR) 0.53 0.62 0.62Insulin 0.56 0.61 0.61 Monocyte Chemotactic Protein 1 0.53 0.61 0.61(MCP1) Prolactin (PRL) 0.59 0.58 0.59 Tetranectin 0.51 0.59 0.59Carcinoembryonic Antigen 0.56 0.61 0.61 Related Cell Adhesion Molecule 1(CEACAM1) 6Ckine 0.50 0.61 0.61 Serum Amyloid P Component (SAP) 0.530.58 0.58 Complement Factor H Related 0.58 0.59 0.59 Protein 1 (CFHR1)Chemokine CC-4 (HCC-4) 0.56 0.58 0.58 Complement C3 (C3) 0.51 0.58 0.58Alpha Fetoprotein (AFP) 0.53 0.59 0.59 Angiopoietin 1 (ANG-1) 0.53 0.590.59 Interleukin 18 (IL18) 0.56 0.62 0.62 Gelsolin 0.58 0.60 0.60Tenascin C (TN-C) 0.58 0.59 0.59 Vitronectin 0.51 0.58 0.58 Beta 2Microglobulin (B2M) 0.52 0.58 0.58 Pancreatic Secretory Trypsin 0.550.60 0.60 Inhibitor (TATI) Matrix Metalloproteinase 3 (MMP3) 0.54 0.590.59 Omentin 0.60 0.59 0.60 Interleukin 18 Binding Protein 0.52 0.600.60 (IL 18bp) Apolipoprotein D (ApoD) 0.54 0.58 0.58 MonoctyeChemotactic Protein 4 0.49 0.60 0.60 (MCP-4) Apolipoprotein E (Apo-E)0.54 0.61 0.61 ST2 0.54 0.58 0.58 Thrombospondin 1 0.53 0.59 0.59Gastric Inhibitory Polypeptide (GIP) 0.55 0.59 0.59 MatrixMetalloproteinase 7 (MMP7) 0.55 0.60 0.60 Intercellular Adhesion 0.510.58 0.58 Molecule 1 (ICAM-1) Dickkopf Related Protein 1 (DKK1) 0.590.59 0.59

TABLE 41 2 Biomarker Multivariate Analysis. Combination of Microalbuminand Analyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Leptin 0.60 0.61 0.61 Eotaxin 2 0.61 0.55 0.61 Insulinlike Growth Factor Binding 0.63 0.57 0.63 Protein 2 (IGFBP2) Resistin0.54 0.59 0.59 Cathepsin D 0.64 0.65 0.65 E-Selectin 0.58 0.65 0.65YKL40 0.57 0.56 0.57 Interleukin 22 (IL22) 0.50 0.59 0.59Cacinoembryonic Antigen (CEA) 0.59 0.57 0.59 Interleukin 8 (IL8) 0.600.45 0.60 Cancer Antigen 15-3 (CA 15-3) 0.54 0.58 0.58 Leptin Receptor(LeptinR) 0.55 0.58 0.58 Insulin 0.65 0.59 0.65 Monocyte ChemotacticProtein 1 0.58 0.61 0.61 (MCP1) Prolactin (PRL) 0.50 0.56 0.56Tetranectin 0.55 0.57 0.57 Carcinoembryonic Antigen 0.56 0.57 0.57Related Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.63 0.62 0.63 SerumAmyloid P Component (SAP) 0.65 0.60 0.65 Complement Factor H Related0.55 0.45 0.55 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.62 0.45 0.62Complement C3 (C3) 0.64 0.56 0.64 Alpha Fetoprotein (AFP) 0.53 0.58 0.58Angiopoietin 1 (ANG-1) 0.57 0.44 0.57 Interleukin 18 (IL18) 0.64 0.630.64 Gelsolin 0.65 0.60 0.65 Tenascin C (TN-C) 0.57 0.58 0.58Vitronectin 0.59 0.56 0.59 Beta 2 Microglobulin (B2M) 0.59 0.56 0.59Pancreatic Secretory Trypsin 0.51 0.45 0.51 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.64 0.59 0.64 Omentin 0.56 0.44 0.56Interleukin 18 Binding Protein 0.58 0.59 0.59 (IL 18bp) Apolipoprotein D(ApoD) 0.66 0.57 0.66 Monoctye Chemotactic Protein 4 0.57 0.45 0.57(MCP-4) Apolipoprotein E (Apo-E) 0.58 0.63 0.63 ST2 0.57 0.45 0.57Thrombospondin 1 0.61 0.58 0.61 Gastric Inhibitory Polypeptide (GIP)0.61 0.59 0.61 Matrix Metalloproteinase 7 (MMP7) 0.63 0.56 0.63Intercellular Adhesion Molecule 1 0.60 0.56 0.60 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.57 0.58 0.58

TABLE 42 2 Biomarker Multivariate Analysis. Combination of Leptin andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Eotaxin 2 0.55 0.61 0.61 Insulin like Growth FactorBinding 0.60 0.60 0.60 Protein 2 (IGFBP2) Resistin 0.60 0.60 0.60Cathepsin D 0.55 0.65 0.65 E-Selectin 0.53 0.67 0.67 YKL40 0.56 0.600.60 Interleukin 22 (IL22) 0.48 0.61 0.61 Cacinoembryonic Antigen (CEA)0.52 0.61 0.61 Interleukin 8 (IL8) 0.51 0.60 0.60 Cancer Antigen 15-3(CA 15-3) 0.46 0.62 0.62 Leptin Receptor (LeptinR) 0.52 0.61 0.61Insulin 0.51 0.63 0.63 Monocyte Chemotactic Protein 1 0.61 0.63 0.63(MCP1) Prolactin (PRL) 0.54 0.60 0.60 Tetranectin 0.61 0.60 0.61Carcinoembryonic Antigen Related 0.52 0.62 0.62 Cell Adhesion Molecule 1(CEACAM1) 6Ckine 0.50 0.62 0.62 Serum Amyloid P Component (SAP) 0.550.60 0.60 Complement Factor H Related 0.48 0.61 0.61 Protein 1 (CFHR1)Chemokine CC-4 (HCC-4) 0.60 0.60 0.60 Complement C3 (C3) 0.50 0.60 0.60Alpha Fetoprotein (AFP) 0.51 0.64 0.64 Angiopoietin 1 (ANG-1) 0.58 0.600.60 Interleukin 18 (IL18) 0.54 0.64 0.64 Gelsolin 0.63 0.61 0.63Tenascin C (TN-C) 0.51 0.60 0.60 Vitronectin 0.55 0.60 0.60 Beta 2Microglobulin (B2M) 0.47 0.60 0.60 Pancreatic Secretory Trypsin 0.560.60 0.60 Inhibitor (TATI) Matrix Metalloproteinase 3 (MMP3) 0.57 0.610.61 Omentin 0.56 0.60 0.60 Interleukin 18 Binding Protein 0.53 0.630.63 (IL 18bp) Apolipoprotein D (ApoD) 0.63 0.60 0.63 MonoctyeChemotactic Protein 4 0.56 0.60 0.60 (MCP-4) Apolipoprotein E (Apo-E)0.58 0.63 0.63 ST2 0.48 0.59 0.59 Thrombospondin 1 0.52 0.59 0.59Gastric Inhibitory Polypeptide (GIP) 0.52 0.63 0.63 MatrixMetalloproteinase 7 (MMP7) 0.52 0.60 0.60 Intercellular AdhesionMolecule 1 0.47 0.61 0.61 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.55 0.60 0.60

TABLE 43 2 Biomarker Multivariate Analysis. Combination of Eotaxin 2 andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Insulin like Growth Factor Binding 0.70 0.58 0.70 Protein2 (IGFBP2) Resistin 0.59 0.55 0.59 Cathepsin D 0.63 0.62 0.63 E-Selectin0.60 0.63 0.63 YKL40 0.58 0.57 0.58 Interleukin 22 (IL22) 0.53 0.60 0.60Cacinoembryonic Antigen (CEA) 0.62 0.57 0.62 Interleukin 8 (IL8) 0.600.58 0.60 Cancer Antigen 15-3 (CA 15-3) 0.51 0.56 0.56 Leptin Receptor(LeptinR) 0.53 0.60 0.60 Insulin 0.69 0.60 0.69 Monocyte ChemotacticProtein 1 0.56 0.59 0.59 (MCP1) Prolactin (PRL) 0.62 0.57 0.62Tetranectin 0.62 0.59 0.62 Carcinoembryonic Antigen Related 0.61 0.570.61 Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.60 0.61 0.61 SerumAmyloid P Component (SAP) 0.62 0.59 0.62 Complement Factor H Related0.56 0.57 0.57 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.55 0.56 0.56Complement C3 (C3) 0.64 0.53 0.64 Alpha Fetoprotein (AFP) 0.60 0.55 0.60Angiopoietin 1 (ANG-1) 0.53 0.55 0.55 Interleukin 18 (IL18) 0.61 0.640.64 Gelsolin 0.62 0.57 0.62 Tenascin C (TN-C) 0.52 0.56 0.56Vitronectin 0.58 0.56 0.58 Beta 2 Microglobulin (B2M) 0.63 0.56 0.63Pancreatic Secretory Trypsin 0.60 0.58 0.60 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.65 0.56 0.65 Omentin 0.52 0.56 0.56Interleukin 18 Binding Protein 0.63 0.58 0.63 (IL 18bp) Apolipoprotein D(ApoD) 0.65 0.58 0.65 Monoctye Chemotactic Protein 4 0.52 0.56 0.56(MCP-4) Apolipoprotein E (Apo-E) 0.59 0.61 0.61 ST2 0.54 0.57 0.57Thrombospondin 1 0.61 0.57 0.61 Gastric Inhibitory Polypeptide (GIP)0.53 0.58 0.58 Matrix Metalloproteinase 7 (MMP7) 0.61 0.56 0.61Intercellular Adhesion Molecule 1 0.66 0.47 0.66 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.60 0.57 0.60

TABLE 44 2 Biomarker Multivariate Analysis. Combination of Insulin likeGrowth Factor Binding Protein 2 (IGFBP2) and Analyte 2 AUC by AUC byrandom logistic Maximum Analyte 2 forest regression AUC Resistin 0.500.59 0.59 Cathepsin D 0.52 0.62 0.62 E-Selectin 0.54 0.64 0.64 YKL400.51 0.57 0.57 Interleukin 22 (IL22) 0.51 0.60 0.60 CacinoembryonicAntigen (CEA) 0.50 0.58 0.58 Interleukin 8 (IL8) 0.55 0.57 0.57 CancerAntigen 15-3 (CA 15-3) 0.52 0.57 0.57 Leptin Receptor (LeptinR) 0.520.59 0.59 Insulin 0.57 0.60 0.60 Monocyte Chemotactic Protein 1 0.590.61 0.61 (MCP1) Prolactin (PRL) 0.52 0.56 0.56 Tetranectin 0.52 0.560.56 Carcinoembryonic Antigen Related 0.53 0.58 0.58 Cell AdhesionMolecule 1 (CEACAM1) 6Ckine 0.52 0.61 0.61 Serum Amyloid P Component(SAP) 0.54 0.58 0.58 Complement Factor H Related 0.52 0.55 0.55 Protein1 (CFHR1) Chemokine CC-4 (HCC-4) 0.55 0.57 0.57 Complement C3 (C3) 0.630.57 0.63 Alpha Fetoprotein (AFP) 0.54 0.59 0.59 Angiopoietin 1 (ANG-1)0.58 0.55 0.58 Interleukin 18 (IL18) 0.56 0.63 0.63 Gelsolin 0.59 0.570.59 Tenascin C (TN-C) 0.55 0.59 0.59 Vitronectin 0.54 0.57 0.57 Beta 2Microglobulin (B2M) 0.60 0.57 0.60 Pancreatic Secretory Trypsin 0.530.56 0.56 Inhibitor (TATI) Matrix Metalloproteinase 3 (MMP3) 0.62 0.580.62 Omentin 0.49 0.57 0.57 Interleukin 18 Binding Protein 0.60 0.590.60 (IL 18bp) Apolipoprotein D (ApoD) 0.61 0.57 0.61 MonoctyeChemotactic Protein 4 0.57 0.57 0.57 (MCP-4) Apolipoprotein E (Apo-E)0.53 0.61 0.61 ST2 0.56 0.55 0.56 Thrombospondin 1 0.55 0.55 0.55Gastric Inhibitory Polypeptide (GIP) 0.62 0.59 0.62 MatrixMetalloproteinase 7 (MMP7) 0.58 0.56 0.58 Intercellular AdhesionMolecule 1 0.54 0.58 0.58 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.49 0.58 0.58

TABLE 45 2 Biomarker Multivariate Analysis. Combination of Resistin andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Cathepsin D 0.56 0.63 0.63 E-Selectin 0.56 0.64 0.64YKL40 0.55 0.45 0.55 Interleukin 22 (IL22) 0.51 0.57 0.57Cacinoembryonic Antigen (CEA) 0.48 0.57 0.57 Interleukin 8 (IL8) 0.530.54 0.54 Cancer Antigen 15-3 (CA 15-3) 0.56 0.59 0.59 Leptin Receptor(LeptinR) 0.45 0.59 0.59 Insulin 0.51 0.64 0.64 Monocyte ChemotacticProtein 1 0.53 0.60 0.60 (MCP1) Prolactin (PRL) 0.55 0.58 0.58Tetranectin 0.49 0.55 0.55 Carcinoembryonic Antigen Related 0.48 0.590.59 Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.58 0.60 0.60 SerumAmyloid P Component (SAP) 0.54 0.58 0.58 Complement Factor H Related0.59 0.55 0.59 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.51 0.46 0.51Complement C3 (C3) 0.58 0.46 0.58 Alpha Fetoprotein (AFP) 0.61 0.61 0.61Angiopoietin 1 (ANG-1) 0.51 0.60 0.60 Interleukin 18 (IL18) 0.51 0.630.63 Gelsolin 0.54 0.57 0.57 Tenascin C (TN-C) 0.52 0.57 0.57Vitronectin 0.53 0.47 0.53 Beta 2 Microglobulin (B2M) 0.52 0.57 0.57Pancreatic Secretory Trypsin 0.52 0.45 0.52 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.51 0.56 0.56 Omentin 0.54 0.55 0.55Interleukin 18 Binding Protein 0.59 0.60 0.60 (IL 18bp) Apolipoprotein D(ApoD) 0.57 0.57 0.57 Monoctye Chemotactic Protein 4 0.54 0.55 0.55(MCP-4) Apolipoprotein E (Apo-E) 0.51 0.61 0.61 ST2 0.51 0.54 0.54Thrombospondin 1 0.51 0.55 0.55 Gastric Inhibitory Polypeptide (GIP)0.54 0.62 0.62 Matrix Metalloproteinase 7 (MMP7) 0.51 0.58 0.58Intercellular Adhesion Molecule 1 0.57 0.56 0.57 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.56 0.45 0.56

TABLE 46 2 Biomarker Multivariate Analysis. Combination of Cathepsin Dand Analyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC E-Selectin 0.55 0.67 0.67 YKL40 0.49 0.62 0.62Interleukin 22 (IL22) 0.51 0.65 0.65 Cacinoembryonic Antigen (CEA) 0.610.63 0.63 Interleukin 8 (IL8) 0.59 0.63 0.63 Cancer Antigen 15-3 (CA15-3) 0.55 0.62 0.62 Leptin Receptor (LeptinR) 0.57 0.64 0.64 Insulin0.62 0.63 0.63 Monocyte Chemotactic Protein 1 0.49 0.64 0.64 (MCP1)Prolactin (PRL) 0.54 0.63 0.63 Tetranectin 0.54 0.63 0.63Carcinoembryonic Antigen Related 0.54 0.64 0.64 Cell Adhesion Molecule 1(CEACAM1) 6Ckine 0.56 0.63 0.63 Serum Amyloid P Component (SAP) 0.520.63 0.63 Complement Factor H Related 0.49 0.63 0.63 Protein 1 (CFHR1)Chemokine CC-4 (HCC-4) 0.54 0.62 0.62 Complement C3 (C3) 0.49 0.62 0.62Alpha Fetoprotein (AFP) 0.57 0.66 0.66 Angiopoietin 1 (ANG-1) 0.54 0.620.62 Interleukin 18 (IL18) 0.54 0.67 0.67 Gelsolin 0.57 0.63 0.63Tenascin C (TN-C) 0.50 0.63 0.63 Vitronectin 0.49 0.63 0.63 Beta 2Microglobulin (B2M) 0.60 0.63 0.63 Pancreatic Secretory Trypsin 0.520.62 0.62 Inhibitor (TATI) Matrix Metalloproteinase 3 (MMP3) 0.57 0.620.62 Omentin 0.60 0.62 0.62 Interleukin 18 Binding Protein 0.51 0.630.63 (IL 18bp) Apolipoprotein D (ApoD) 0.59 0.62 0.62 MonoctyeChemotactic Protein 4 0.50 0.62 0.62 (MCP-4) Apolipoprotein E (Apo-E)0.55 0.64 0.64 ST2 0.53 0.62 0.62 Thrombospondin 1 0.56 0.62 0.62Gastric Inhibitory Polypeptide (GIP) 0.48 0.63 0.63 MatrixMetalloproteinase 7 (MMP7) 0.53 0.63 0.63 Intercellular AdhesionMolecule 1 0.53 0.62 0.62 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.55 0.63 0.63

TABLE 47 2 Biomarker Multivariate Analysis. Combination of E-Selectinand Analyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC YKL40 0.46 0.63 0.63 Interleukin 22 (IL22) 0.55 0.65 0.65Cacinoembryonic Antigen (CEA) 0.52 0.63 0.63 Interleukin 8 (IL8) 0.550.64 0.64 Cancer Antigen 15-3 (CA 15-3) 0.55 0.66 0.66 Leptin Receptor(LeptinR) 0.52 0.65 0.65 Insulin 0.59 0.66 0.66 Monocyte ChemotacticProtein 1 0.51 0.65 0.65 (MCP1) Prolactin (PRL) 0.55 0.64 0.64Tetranectin 0.55 0.64 0.64 Carcinoembryonic Antigen Related 0.58 0.670.67 Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.53 0.66 0.66 SerumAmyloid P Component (SAP) 0.54 0.65 0.65 Complement Factor H Related0.49 0.63 0.63 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.49 0.63 0.63Complement C3 (C3) 0.49 0.63 0.63 Alpha Fetoprotein (AFP) 0.50 0.67 0.67Angiopoietin 1 (ANG-1) 0.49 0.63 0.63 Interleukin 18 (IL18) 0.51 0.660.66 Gelsolin 0.60 0.64 0.64 Tenascin C (TN-C) 0.49 0.63 0.63Vitronectin 0.51 0.64 0.64 Beta 2 Microglobulin (B2M) 0.54 0.64 0.64Pancreatic Secretory Trypsin 0.56 0.63 0.63 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.59 0.65 0.65 Omentin 0.58 0.63 0.63Interleukin 18 Binding Protein 0.50 0.64 0.64 (IL 18bp) Apolipoprotein D(ApoD) 0.62 0.65 0.65 Monoctye Chemotactic Protein 4 0.59 0.63 0.63(MCP-4) Apolipoprotein E (Apo-E) 0.55 0.65 0.65 ST2 0.56 0.64 0.64Thrombospondin 1 0.49 0.64 0.64 Gastric Inhibitory Polypeptide (GIP)0.51 0.66 0.66 Matrix Metalloproteinase 7 (MMP7) 0.56 0.64 0.64Intercellular Adhesion Molecule 1 0.52 0.63 0.63 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.54 0.64 0.64

TABLE 48 2 Biomarker Multivariate Analysis. Combination of YKL40 andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Interleukin 22 (IL22) 0.58 0.55 0.58 CacinoembryonicAntigen (CEA) 0.49 0.55 0.55 Interleukin 8 (IL8) 0.52 0.52 0.52 CancerAntigen 15-3 (CA 15-3) 0.61 0.55 0.61 Leptin Receptor (LeptinR) 0.520.59 0.59 Insulin 0.58 0.61 0.61 Monocyte Chemotactic Protein 1 0.490.58 0.58 (MCP1) Prolactin (PRL) 0.55 0.58 0.58 Tetranectin 0.53 0.530.53 Carcinoembryonic Antigen Related 0.54 0.57 0.57 Cell AdhesionMolecule 1 (CEACAM1) 6Ckine 0.56 0.60 0.60 Serum Amyloid P Component(SAP) 0.55 0.57 0.57 Complement Factor H Related 0.58 0.52 0.58 Protein1 (CFHR1) Chemokine CC-4 (HCC-4) 0.48 0.50 0.50 Complement C3 (C3) 0.510.51 0.51 Alpha Fetoprotein (AFP) 0.55 0.44 0.55 Angiopoietin 1 (ANG-1)0.55 0.55 0.55 Interleukin 18 (IL18) 0.62 0.63 0.63 Gelsolin 0.51 0.530.53 Tenascin C (TN-C) 0.52 0.52 0.52 Vitronectin 0.52 0.49 0.52 Beta 2Microglobulin (B2M) 0.58 0.56 0.58 Pancreatic Secretory Trypsin 0.550.52 0.55 Inhibitor (TATI) Matrix Metalloproteinase 3 (MMP3) 0.60 0.540.60 Omentin 0.56 0.52 0.56 Interleukin 18 Binding Protein 0.52 0.580.58 (IL 18bp) Apolipoprotein D (ApoD) 0.62 0.56 0.62 MonoctyeChemotactic Protein 4 0.57 0.55 0.57 (MCP-4) Apolipoprotein E (Apo-E)0.55 0.60 0.60 ST2 0.53 0.52 0.53 Thrombospondin 1 0.53 0.50 0.53Gastric Inhibitory Polypeptide (GIP) 0.46 0.59 0.59 MatrixMetalloproteinase 7 (MMP7) 0.54 0.56 0.56 Intercellular AdhesionMolecule 1 0.59 0.49 0.59 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.60 0.49 0.60

TABLE 49 2 Biomarker Multivariate Analysis. Combination of Interleukin22 (IL22) and Analyte 2 AUC by AUC by random logistic Maximum Analyte 2forest regression AUC Cacinoembryonic Antigen (CEA) 0.63 0.58 0.63Interleukin 8 (IL8) 0.56 0.57 0.57 Cancer Antigen 15-3 (CA 15-3) 0.670.59 0.67 Leptin Receptor (LeptinR) 0.53 0.60 0.60 Insulin 0.55 0.610.61 Monocyte Chemotactic Protein 1 0.54 0.60 0.60 (MCP1) Prolactin(PRL) 0.51 0.60 0.60 Tetranectin 0.57 0.58 0.58 Carcinoembryonic AntigenRelated 0.59 0.59 0.59 Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.550.62 0.62 Serum Amyloid P Component (SAP) 0.52 0.60 0.60 ComplementFactor H Related 0.53 0.59 0.59 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4)0.52 0.57 0.57 Complement C3 (C3) 0.53 0.57 0.57 Alpha Fetoprotein (AFP)0.52 0.57 0.57 Angiopoietin 1 (ANG-1) 0.51 0.58 0.58 Interleukin 18(IL18) 0.49 0.64 0.64 Gelsolin 0.53 0.57 0.57 Tenascin C (TN-C) 0.520.43 0.52 Vitronectin 0.49 0.56 0.56 Beta 2 Microglobulin (B2M) 0.530.57 0.57 Pancreatic Secretory Trypsin 0.56 0.56 0.56 Inhibitor (TATI)Matrix Metalloproteinase 3 (MMP3) 0.52 0.58 0.58 Omentin 0.67 0.57 0.67Interleukin 18 Binding Protein 0.52 0.60 0.60 (IL 18bp) Apolipoprotein D(ApoD) 0.56 0.57 0.57 Monoctye Chemotactic Protein 4 0.58 0.57 0.58(MCP-4) Apolipoprotein E (Apo-E) 0.56 0.62 0.62 ST2 0.54 0.57 0.57Thrombospondin 1 0.54 0.57 0.57 Gastric Inhibitory Polypeptide (GIP)0.52 0.60 0.60 Matrix Metalloproteinase 7 (MMP7) 0.53 0.57 0.57Intercellular Adhesion Molecule 1 0.70 0.57 0.70 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.56 0.58 0.58

TABLE 50 2 Biomarker Multivariate Analysis. Combination ofCarcinoembryonic Antigen (CEA) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC Interleukin 8 (IL8)0.51 0.56 0.56 Cancer Antigen 15-3 (CA 15-3) 0.62 0.56 0.62 LeptinReceptor (LeptinR) 0.59 0.59 0.59 Insulin 0.50 0.64 0.64 MonocyteChemotactic Protein 1 0.49 0.59 0.59 (MCP1) Prolactin (PRL) 0.62 0.590.62 Tetranectin 0.55 0.56 0.56 Carcinoembryonic Antigen Related 0.520.57 0.57 Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.53 0.61 0.61 SerumAmyloid P Component (SAP) 0.52 0.58 0.58 Complement Factor H Related0.51 0.56 0.56 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.51 0.54 0.54Complement C3 (C3) 0.58 0.56 0.58 Alpha Fetoprotein (AFP) 0.65 0.63 0.65Angiopoietin 1 (ANG-1) 0.52 0.57 0.57 Interleukin 18 (IL18) 0.59 0.630.63 Gelsolin 0.50 0.57 0.57 Tenascin C (TN-C) 0.57 0.45 0.57Vitronectin 0.59 0.56 0.59 Beta 2 Microglobulin (B2M) 0.49 0.56 0.56Pancreatic Secretory Trypsin 0.61 0.56 0.61 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.58 0.45 0.58 Omentin 0.63 0.57 0.63Interleukin 18 Binding Protein 0.57 0.58 0.58 (IL 18bp) Apolipoprotein D(ApoD) 0.58 0.56 0.58 Monoctye Chemotactic Protein 4 0.45 0.56 0.56(MCP-4) Apolipoprotein E (Apo-E) 0.56 0.62 0.62 ST2 0.50 0.58 0.58Thrombospondin 1 0.52 0.55 0.55 Gastric Inhibitory Polypeptide (GIP)0.54 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.48 0.58 0.58Intercellular Adhesion Molecule 1 0.59 0.55 0.59 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.54 0.46 0.54

TABLE 51 2 Biomarker Multivariate Analysis. Combination of Interleukin 8(IL8) and Analyte 2 AUC by AUC by random logistic Maximum Analyte 2forest regression AUC Cancer Antigen 15-3 (CA 15-3) 0.52 0.54 0.54Leptin Receptor (LeptinR) 0.60 0.59 0.60 Insulin 0.55 0.61 0.61 MonocyteChemotactic Protein 1 0.58 0.59 0.59 (MCP1) Prolactin (PRL) 0.52 0.560.56 Tetranectin 0.58 0.52 0.58 Carcinoembryonic Antigen Related 0.570.57 0.57 Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.54 0.61 0.61 SerumAmyloid P Component (SAP) 0.42 0.58 0.58 Complement Factor H Related0.60 0.50 0.60 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.54 0.50 0.54Complement C3 (C3) 0.61 0.52 0.61 Alpha Fetoprotein (AFP) 0.53 0.57 0.57Angiopoietin 1 (ANG-1) 0.59 0.54 0.59 Interleukin 18 (IL18) 0.52 0.630.63 Gelsolin 0.61 0.46 0.61 Tenascin C (TN-C) 0.50 0.53 0.53Vitronectin 0.47 0.50 0.50 Beta 2 Microglobulin (B2M) 0.62 0.56 0.62Pancreatic Secretory Trypsin 0.56 0.55 0.56 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.60 0.54 0.60 Omentin 0.55 0.52 0.55Interleukin 18 Binding Protein 0.57 0.58 0.58 (IL 18bp) Apolipoprotein D(ApoD) 0.64 0.55 0.64 Monoctye Chemotactic Protein 4 0.52 0.54 0.54(MCP-4) Apolipoprotein E (Apo-E) 0.62 0.60 0.62 ST2 0.63 0.49 0.63Thrombospondin 1 0.54 0.53 0.54 Gastric Inhibitory Polypeptide (GIP)0.56 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.51 0.56 0.56Intercellular Adhesion Molecule 1 0.56 0.50 0.56 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.57 0.48 0.57

TABLE 52 2 Biomarker Multivariate Analysis. Combination of CancerAntigen 15-3 (CA 15-3) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Leptin Receptor (LeptinR) 0.590.58 0.59 Insulin 0.62 0.61 0.62 Monocyte Chemotactic Protein 1 0.520.61 0.61 (MCP1) Prolactin (PRL) 0.67 0.57 0.67 Tetranectin 0.61 0.540.61 Carcinoembryonic Antigen Related 0.56 0.58 0.58 Cell AdhesionMolecule 1 (CEACAM1) 6Ckine 0.49 0.63 0.63 Serum Amyloid P Component(SAP) 0.50 0.59 0.59 Complement Factor H Related 0.51 0.54 0.54 Protein1 (CFHR1) Chemokine CC-4 (HCC-4) 0.61 0.54 0.61 Complement C3 (C3) 0.460.57 0.57 Alpha Fetoprotein (AFP) 0.61 0.59 0.61 Angiopoietin 1 (ANG-1)0.59 0.56 0.59 Interleukin 18 (IL18) 0.55 0.65 0.65 Gelsolin 0.52 0.550.55 Tenascin C (TN-C) 0.56 0.55 0.56 Vitronectin 0.56 0.55 0.56 Beta 2Microglobulin (B2M) 0.50 0.56 0.56 Pancreatic Secretory Trypsin 0.550.55 0.55 Inhibitor (TATI) Matrix Metalloproteinase 3 (MMP3) 0.47 0.560.56 Omentin 0.60 0.53 0.60 Interleukin 18 Binding Protein 0.55 0.580.58 (IL 18bp) Apolipoprotein D (ApoD) 0.55 0.55 0.55 MonoctyeChemotactic Protein 4 0.59 0.55 0.59 (MCP-4) Apolipoprotein E (Apo-E)0.56 0.61 0.61 ST2 0.51 0.54 0.54 Thrombospondin 1 0.56 0.55 0.56Gastric Inhibitory Polypeptide (GIP) 0.69 0.60 0.69 MatrixMetalloproteinase 7 (MMP7) 0.54 0.57 0.57 Intercellular AdhesionMolecule 1 0.61 0.55 0.61 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.67 0.55 0.67

TABLE 53 2 Biomarker Multivariate Analysis. Combination of LeptinReceptor (LeptinR) and Analyte 2 AUC by AUC by random logistic MaximumAnalyte 2 forest regression AUC Insulin 0.55 0.62 0.62 MonocyteChemotactic Protein 1 0.55 0.61 0.61 (MCP1) Prolactin (PRL) 0.64 0.580.64 Tetranectin 0.58 0.58 0.58 Carcinoembryonic Antigen Related 0.550.59 0.59 Cell Adhesion Molecule 1 (CEACAM1) 6Ckine 0.58 0.61 0.61 SerumAmyloid P Component (SAP) 0.47 0.60 0.60 Complement Factor H Related0.55 0.58 0.58 Protein 1 (CFHR1) Chemokine CC-4 (HCC-4) 0.51 0.60 0.60Complement C3 (C3) 0.55 0.60 0.60 Alpha Fetoprotein (AFP) 0.52 0.59 0.59Angiopoietin 1 (ANG-1) 0.56 0.58 0.58 Interleukin 18 (IL18) 0.58 0.640.64 Gelsolin 0.48 0.59 0.59 Tenascin C (TN-C) 0.57 0.59 0.59Vitronectin 0.57 0.60 0.60 Beta 2 Microglobulin (B2M) 0.58 0.58 0.58Pancreatic Secretory Trypsin 0.55 0.58 0.58 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.52 0.60 0.60 Omentin 0.55 0.58 0.58Interleukin 18 Binding Protein 0.53 0.61 0.61 (IL 18bp) Apolipoprotein D(ApoD) 0.60 0.60 0.60 Monoctye Chemotactic Protein 4 0.54 0.58 0.58(MCP-4) Apolipoprotein E (Apo-E) 0.53 0.64 0.64 ST2 0.59 0.58 0.59Thrombospondin 1 0.62 0.57 0.62 Gastric Inhibitory Polypeptide (GIP)0.54 0.61 0.61 Matrix Metalloproteinase 7 (MMP7) 0.53 0.59 0.59Intercellular Adhesion Molecule 1 0.51 0.60 0.60 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.59 0.61 0.61

TABLE 54 2 Biomarker Multivariate Analysis. Combination of Insulin andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Monocyte Chemotactic Protein 1 0.53 0.62 0.62 (MCP1)Prolactin (PRL) 0.58 0.63 0.63 Tetranectin 0.56 0.61 0.61Carcinoembryonic Antigen Related 0.49 0.64 0.64 Cell Adhesion Molecule 1(CEACAM1) 6Ckine 0.53 0.61 0.61 Serum Amyloid P Component (SAP) 0.540.59 0.59 Complement Factor H Related Protein 1 0.53 0.62 0.62 (CFHR1)Chemokine CC-4 (HCC-4) 0.53 0.60 0.60 Complement C3 (C3) 0.59 0.60 0.60Alpha Fetoprotein (AFP) 0.50 0.60 0.60 Angiopoietin 1 (ANG-1) 0.58 0.630.63 Interleukin 18 (IL18) 0.56 0.64 0.64 Gelsolin 0.63 0.64 0.64Tenascin C (TN-C) 0.55 0.57 0.57 Vitronectin 0.56 0.60 0.60 Beta 2Microglobulin (B2M) 0.48 0.61 0.61 Pancreatic Secretory TrypsinInhibitor 0.52 0.61 0.61 (TATI) Matrix Metalloproteinase 3 (MMP3) 0.550.58 0.58 Omentin 0.47 0.60 0.60 Interleukin 18 Binding Protein 0.520.60 0.60 (IL 18 bp) Apolipoprotein D (ApoD) 0.60 0.62 0.62 MonoctyeChemotactic Protein 4 0.49 0.63 0.63 (MCP-4) Apolipoprotein E (Apo-E)0.53 0.60 0.60 ST2 0.54 0.60 0.60 Thrombospondin 1 0.56 0.62 0.62Gastric Inhibitory Polypeptide (GIP) 0.53 0.64 0.64 MatrixMetalloproteinase 7 (MMP7) 0.61 0.64 0.64 Intercellular AdhesionMolecule 1 0.48 0.62 0.62 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.52 0.58 0.58

TABLE 55 2 Biomarker Multivariate Analysis. Combination of MonocyteChemotactic Protein 1 (MCP1) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Prolactin (PRL) 0.50 0.60 0.60Tetranectin 0.57 0.60 0.60 Carcinoembryonic Antigen Related Cell 0.620.62 0.62 Adhesion Molecule 1 (CEACAM1) 6Ckine 0.52 0.63 0.63 SerumAmyloid P Component (SAP) 0.58 0.60 0.60 Complement Factor H RelatedProtein 1 0.56 0.58 0.58 (CFHR1) Chemokine CC-4 (HCC-4) 0.53 0.58 0.58Complement C3 (C3) 0.59 0.59 0.59 Alpha Fetoprotein (AFP) 0.52 0.62 0.62Angiopoietin 1 (ANG-1) 0.61 0.59 0.61 Interleukin 18 (IL18) 0.59 0.650.65 Gelsolin 0.63 0.61 0.63 Tenascin C (TN-C) 0.56 0.56 0.56Vitronectin 0.62 0.58 0.62 Beta 2 Microglobulin (B2M) 0.56 0.60 0.60Pancreatic Secretory Trypsin Inhibitor 0.50 0.58 0.58 (TATI) MatrixMetalloproteinase 3 (MMP3) 0.56 0.60 0.60 Omentin 0.51 0.57 0.57Interleukin 18 Binding Protein 0.53 0.61 0.61 (IL 18 bp) ApolipoproteinD (ApoD) 0.64 0.59 0.64 Monoctye Chemotactic Protein 4 0.52 0.58 0.58(MCP-4) Apolipoprotein E (Apo-E) 0.59 0.62 0.62 ST2 0.58 0.58 0.58Thrombospondin 1 0.58 0.58 0.58 Gastric Inhibitory Polypeptide (GIP)0.50 0.61 0.61 Matrix Metalloproteinase 7 (MMP7) 0.55 0.60 0.60Intercellular Adhesion Molecule 1 0.53 0.59 0.59 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.52 0.58 0.58

TABLE 56 2 Biomarker Multivariate Analysis. Combination of Prolactin(PRL) and Analyte 2 AUC by AUC by random logistic Maximum Analyte 2forest regression AUC Tetranectin 0.47 0.57 0.57 CarcinoembryonicAntigen Related Cell 0.54 0.58 0.58 Adhesion Molecule 1 (CEACAM1) 6Ckine0.52 0.60 0.60 Serum Amyloid P Component (SAP) 0.49 0.59 0.59 ComplementFactor H Related Protein 1 0.57 0.56 0.57 (CFHR1) Chemokine CC-4 (HCC-4)0.52 0.55 0.55 Complement C3 (C3) 0.47 0.55 0.55 Alpha Fetoprotein (AFP)0.57 0.59 0.59 Angiopoietin 1 (ANG-1) 0.54 0.54 0.54 Interleukin 18(IL18) 0.53 0.63 0.63 Gelsolin 0.49 0.59 0.59 Tenascin C (TN-C) 0.550.56 0.56 Vitronectin 0.51 0.54 0.54 Beta 2 Microglobulin (B2M) 0.520.55 0.55 Pancreatic Secretory Trypsin Inhibitor 0.57 0.57 0.57 (TATI)Matrix Metalloproteinase 3 (MMP3) 0.51 0.57 0.57 Omentin 0.71 0.55 0.71Interleukin 18 Binding Protein 0.46 0.60 0.60 (IL 18 bp) ApolipoproteinD (ApoD) 0.56 0.56 0.56 Monoctye Chemotactic Protein 4 0.62 0.57 0.62(MCP-4) Apolipoprotein E (Apo-E) 0.53 0.61 0.61 ST2 0.53 0.56 0.56Thrombospondin 1 0.47 0.56 0.56 Gastric Inhibitory Polypeptide (GIP)0.52 0.64 0.64 Matrix Metalloproteinase 7 (MMP7) 0.55 0.54 0.55Intercellular Adhesion Molecule 1 0.62 0.46 0.62 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.62 0.56 0.62

TABLE 57 2 Biomarker Multivariate Analysis. Combination of Tetranectinand Analyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Carcinoembryonic Antigen Related Cell 0.50 0.58 0.58Adhesion Molecule 1 (CEACAM1) 6Ckine 0.54 0.61 0.61 Serum Amyloid PComponent (SAP) 0.51 0.58 0.58 Complement Factor H Related Protein 10.49 0.49 0.49 (CFHR1) Chemokine CC-4 (HCC-4) 0.54 0.49 0.54 ComplementC3 (C3) 0.54 0.52 0.54 Alpha Fetoprotein (AFP) 0.54 0.58 0.58Angiopoietin 1 (ANG-1) 0.54 0.47 0.54 Interleukin 18 (IL18) 0.56 0.630.63 Gelsolin 0.56 0.47 0.56 Tenascin C (TN-C) 0.59 0.52 0.59Vitronectin 0.49 0.50 0.50 Beta 2 Microglobulin (B2M) 0.50 0.58 0.58Pancreatic Secretory Trypsin Inhibitor 0.60 0.51 0.60 (TATI) MatrixMetalloproteinase 3 (MMP3) 0.55 0.56 0.56 Omentin 0.49 0.52 0.52Interleukin 18 Binding Protein 0.53 0.58 0.58 (IL 18 bp) ApolipoproteinD (ApoD) 0.58 0.57 0.58 Monoctye Chemotactic Protein 4 0.58 0.56 0.58(MCP-4) Apolipoprotein E (Apo-E) 0.56 0.60 0.60 ST2 0.46 0.53 0.53Thrombospondin 1 0.49 0.52 0.52 Gastric Inhibitory Polypeptide (GIP)0.55 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.46 0.56 0.56Intercellular Adhesion Molecule 1 0.63 0.49 0.63 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.59 0.48 0.59

TABLE 58 2 Biomarker Multivariate Analysis. Combination ofCarcinoembryonic Antigen Related Cell Adhesion Molecule 1 (CEACAM1) andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC 6Ckine 0.57 0.61 0.61 Serum Amyloid P Component (SAP)0.55 0.60 0.60 Complement Factor H Related Protein 1 0.55 0.56 0.56(CFHR1) Chemokine CC-4 (HCC-4) 0.56 0.57 0.57 Complement C3 (C3) 0.590.58 0.59 Alpha Fetoprotein (AFP) 0.56 0.59 0.59 Angiopoietin 1 (ANG-1)0.51 0.44 0.51 Interleukin 18 (IL18) 0.60 0.66 0.66 Gelsolin 0.58 0.600.60 Tenascin C (TN-C) 0.53 0.57 0.57 Vitronectin 0.55 0.58 0.58 Beta 2Microglobulin (B2M) 0.55 0.59 0.59 Pancreatic Secretory TrypsinInhibitor 0.53 0.56 0.56 (TATI) Matrix Metalloproteinase 3 (MMP3) 0.600.61 0.61 Omentin 0.54 0.57 0.57 Interleukin 18 Binding Protein 0.590.62 0.62 (IL 18bp) Apolipoprotein D (ApoD) 0.57 0.58 0.58 MonoctyeChemotactic Protein 4 0.56 0.57 0.57 (MCP-4) Apolipoprotein E (Apo-E)0.63 0.65 0.65 ST2 0.55 0.57 0.57 Thrombospondin 1 0.49 0.57 0.57Gastric Inhibitory Polypeptide (GIP) 0.53 0.62 0.62 MatrixMetalloproteinase 7 (MMP7) 0.51 0.58 0.58 Intercellular AdhesionMolecule 1 0.57 0.58 0.58 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.49 0.59 0.59

TABLE 59 2 Biomarker Multivariate Analysis. Combination of 6Ckine andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Serum Amyloid P Component (SAP) 0.57 0.61 0.61 ComplementFactor H Related Protein 1 0.57 0.60 0.60 (CFHR1) Chemokine CC-4 (HCC-4)0.48 0.61 0.61 Complement C3 (C3) 0.56 0.60 0.60 Alpha Fetoprotein (AFP)0.56 0.61 0.61 Angiopoietin 1 (ANG-1) 0.52 0.61 0.61 Interleukin 18(IL18) 0.47 0.64 0.64 Gelsolin 0.55 0.60 0.60 Tenascin C (TN-C) 0.540.62 0.62 Vitronectin 0.63 0.61 0.63 Beta 2 Microglobulin (B2M) 0.520.61 0.61 Pancreatic Secretory Trypsin Inhibitor 0.54 0.61 0.61 (TATI)Matrix Metalloproteinase 3 (MMP3) 0.62 0.60 0.62 Omentin 0.56 0.60 0.60Interleukin 18 Binding Protein 0.49 0.61 0.61 (IL 18bp) Apolipoprotein D(ApoD) 0.58 0.61 0.61 Monoctye Chemotactic Protein 4 0.52 0.60 0.60(MCP-4) Apolipoprotein E (Apo-E) 0.46 0.62 0.62 ST2 0.58 0.61 0.61Thrombospondin 1 0.54 0.60 0.60 Gastric Inhibitory Polypeptide (GIP)0.51 0.62 0.62 Matrix Metalloproteinase 7 (MMP7) 0.54 0.61 0.61Intercellular Adhesion Molecule 1 0.55 0.61 0.61 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.56 0.59 0.59

TABLE 60 2 Biomarker Multivariate Analysis. Combination of Serum AmyloidP Component (SAP) and Analyte 2 AUC by AUC by random logistic MaximumAnalyte 2 forest regression AUC Complement Factor H Related Protein 10.53 0.59 0.59 (CFHR1) Chemokine CC-4 (HCC-4) 0.50 0.57 0.57 ComplementC3 (C3) 0.61 0.57 0.61 Alpha Fetoprotein (AFP) 0.55 0.61 0.61Angiopoietin 1 (ANG-1) 0.61 0.58 0.61 Interleukin 18 (IL18) 0.48 0.620.62 Gelsolin 0.56 0.60 0.60 Tenascin C (TN-C) 0.54 0.59 0.59Vitronectin 0.54 0.58 0.58 Beta 2 Microglobulin (B2M) 0.53 0.57 0.57Pancreatic Secretory Trypsin Inhibitor 0.52 0.58 0.58 (TATI) MatrixMetalloproteinase 3 (MMP3) 0.55 0.59 0.59 Omentin 0.50 0.57 0.57Interleukin 18 Binding Protein 0.56 0.60 0.60 (IL 18bp) Apolipoprotein D(ApoD) 0.61 0.58 0.61 Monoctye Chemotactic Protein 4 0.49 0.58 0.58(MCP-4) Apolipoprotein E (Apo-E) 0.60 0.62 0.62 ST2 0.55 0.57 0.57Thrombospondin 1 0.55 0.57 0.57 Gastric Inhibitory Polypeptide (GIP)0.52 0.60 0.60 Matrix Metalloproteinase 7 (MMP7) 0.58 0.58 0.58Intercellular Adhesion Molecule 1 0.50 0.58 0.58 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.51 0.59 0.59

TABLE 61 2 Biomarker Multivariate Analysis. Combination of ComplementFactor H Related Protein 1 (CFHR1) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC Chemokine CC-4 (HCC-4)0.52 0.50 0.52 Complement C3 (C3) 0.55 0.48 0.55 Alpha Fetoprotein (AFP)0.50 0.57 0.57 Angiopoietin 1 (ANG-1) 0.58 0.54 0.58 Interleukin 18(IL18) 0.55 0.64 0.64 Gelsolin 0.56 0.46 0.56 Tenascin C (TN-C) 0.500.53 0.53 Vitronectin 0.57 0.50 0.57 Beta 2 Microglobulin (B2M) 0.530.45 0.53 Pancreatic Secretory Trypsin Inhibitor 0.56 0.50 0.56 (TATI)Matrix Metalloproteinase 3 (MMP3) 0.62 0.54 0.62 Omentin 0.62 0.54 0.62Interleukin 18 Binding Protein 0.45 0.58 0.58 (IL 18bp) Apolipoprotein D(ApoD) 0.55 0.54 0.55 Monoctye Chemotactic Protein 4 0.49 0.54 0.54(MCP-4) Apolipoprotein E (Apo-E) 0.56 0.60 0.60 ST2 0.57 0.52 0.57Thrombospondin 1 0.56 0.53 0.56 Gastric Inhibitory Polypeptide (GIP)0.52 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.49 0.56 0.56Intercellular Adhesion Molecule 1 0.60 0.51 0.60 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.48 0.49 0.49

TABLE 62 2 Biomarker Multivariate Analysis. Combination of ChemokineCC-4 (HCC-4) and Analyte 2 AUC by AUC by random logistic Maximum Analyte2 forest regression AUC Complement C3 (C3) 0.55 0.51 0.55 AlphaFetoprotein (AFP) 0.47 0.53 0.53 Angiopoietin 1 (ANG-1) 0.56 0.53 0.56Interleukin 18 (IL18) 0.56 0.63 0.63 Gelsolin 0.56 0.54 0.56 Tenascin C(TN-C) 0.49 0.54 0.54 Vitronectin 0.61 0.51 0.61 Beta 2 Microglobulin(B2M) 0.50 0.56 0.56 Pancreatic Secretory Trypsin Inhibitor 0.58 0.510.58 (TATI) Matrix Metalloproteinase 3 (MMP3) 0.58 0.55 0.58 Omentin0.54 0.53 0.54 Interleukin 18 Binding Protein 0.51 0.58 0.58 (IL 18bp)Apolipoprotein D (ApoD) 0.59 0.55 0.59 Monoctye Chemotactic Protein 40.51 0.48 0.51 (MCP-4) Apolipoprotein E (Apo-E) 0.50 0.60 0.60 ST2 0.540.51 0.54 Thrombospondin 1 0.54 0.51 0.54 Gastric Inhibitory Polypeptide(GIP) 0.52 0.60 0.60 Matrix Metalloproteinase 7 (MMP7) 0.57 0.56 0.57Intercellular Adhesion Molecule 1 0.60 0.51 0.60 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.50 0.49 0.50

TABLE 63 2 Biomarker Multivariate Analysis. Combination of Complement C3(C3) and Analyte 2 AUC by AUC by Maxi- random logistic mum Analyte 2forest regression AUC Alpha Fetoprotein (AFP) 0.55 0.57 0.57Angiopoietin 1 (ANG-1) 0.54 0.55 0.55 Interleukin 18 (IL18) 0.52 0.620.62 Gelsolin 0.64 0.54 0.64 Tenascin C (TN-C) 0.54 0.55 0.55Vitronectin 0.47 0.51 0.51 Beta 2 Microglobulin (B2M) 0.58 0.54 0.58Pancreatic Secretory Trypsin 0.49 0.51 0.51 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.56 0.57 0.57 Omentin 0.52 0.53 0.53Interleukin 18 Binding Protein (IL 18 bp) 0.55 0.58 0.58 ApolipoproteinD (ApoD) 0.70 0.56 0.70 Monoctye Chemotactic Protein 4 (MCP-4) 0.53 0.540.54 Apolipoprotein E (Apo-E) 0.59 0.61 0.61 ST2 0.52 0.51 0.52Thrombospondin 1 0.66 0.49 0.66 Gastric Inhibitory Polypeptide (GIP)0.56 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.63 0.56 0.63Intercellular Adhesion Molecule 0.57 0.52 0.57 1 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.51 0.53 0.53

TABLE 64 2 Biomarker Multivariate Analysis. Combination of AlphaFetoprotein (AFP) and Analyte 2 AUC by AUC by random logistic MaximumAnalyte 2 forest regression AUC Angiopoietin 1 (ANG-1) 0.50 0.55 0.55Interleukin 18 (IL18) 0.48 0.66 0.66 Gelsolin 0.61 0.60 0.61 Tenascin C(TN-C) 0.52 0.56 0.56 Vitronectin 0.52 0.57 0.57 Beta 2 Microglobulin(B2M) 0.55 0.59 0.59 Pancreatic Secretory Trypsin Inhibitor 0.56 0.540.56 (TATI) Matrix Metalloproteinase 3 (MMP3) 0.49 0.57 0.57 Omentin0.54 0.56 0.56 Interleukin 18 Binding Protein 0.55 0.62 0.62 (IL 18bp)Apolipoprotein D (ApoD) 0.59 0.46 0.59 Monoctye Chemotactic Protein 40.57 0.56 0.57 (MCP-4) Apolipoprotein E (Apo-E) 0.60 0.63 0.63 ST2 0.540.55 0.55 Thrombospondin 1 0.52 0.55 0.55 Gastric Inhibitory Polypeptide(GIP) 0.59 0.58 0.59 Matrix Metalloproteinase 7 (MMP7) 0.53 0.61 0.61Intercellular Adhesion Molecule 1 0.52 0.56 0.56 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.60 0.56 0.60

TABLE 65 2 Biomarker Multivariate Analysis. Combination of Angiopoietin1 (ANG-1) and Analyte 2 AUC by AUC by random logistic Maximum Analyte 2forest regression AUC Interleukin 18 (IL18) 0.62 0.62 0.62 Gelsolin 0.580.55 0.58 Tenascin C (TN-C) 0.58 0.56 0.58 Vitronectin 0.56 0.54 0.56Beta 2 Microglobulin (B2M) 0.50 0.57 0.57 Pancreatic Secretory TrypsinInhibitor 0.55 0.54 0.55 (TATI) Matrix Metalloproteinase 3 (MMP3) 0.560.55 0.56 Omentin 0.59 0.54 0.59 Interleukin 18 Binding Protein 0.590.56 0.59 (IL 18bp) Apolipoprotein D (ApoD) 0.62 0.56 0.62 MonoctyeChemotactic Protein 4 0.55 0.57 0.57 (MCP-4) Apolipoprotein E (Apo-E)0.49 0.60 0.60 ST2 0.61 0.54 0.61 Thrombospondin 1 0.59 0.54 0.59Gastric Inhibitory Polypeptide (GIP) 0.50 0.59 0.59 MatrixMetalloproteinase 7 (MMP7) 0.62 0.58 0.62 Intercellular AdhesionMolecule 1 0.52 0.52 0.52 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.53 0.56 0.56

TABLE 66 2 Biomarker Multivariate Analysis. Combination of Interleukin18 (IL18) and Analyte 2 AUC by AUC by random logistic Maximum Analyte 2forest regression AUC Gelsolin 0.47 0.63 0.63 Tenascin C (TN-C) 0.560.65 0.65 Vitronectin 0.56 0.63 0.63 Beta 2 Microglobulin (B2M) 0.500.62 0.62 Pancreatic Secretory Trypsin Inhibitor 0.57 0.63 0.63 (TATI)Matrix Metalloproteinase 3 (MMP3) 0.60 0.63 0.63 Omentin 0.54 0.63 0.63Interleukin 18 Binding Protein 0.58 0.61 0.61 (IL 18bp) Apolipoprotein D(ApoD) 0.59 0.63 0.63 Monoctye Chemotactic Protein 4 0.48 0.64 0.64(MCP-4) Apolipoprotein E (Apo-E) 0.51 0.64 0.64 ST2 0.55 0.63 0.63Thrombospondin 1 0.55 0.63 0.63 Gastric Inhibitory Polypeptide (GIP)0.53 0.63 0.63 Matrix Metalloproteinase 7 (MMP7) 0.59 0.63 0.63Intercellular Adhesion Molecule 1 0.57 0.63 0.63 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.54 0.63 0.63

TABLE 67 2 Biomarker Multivariate Analysis. Combination of Gelsolin andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Tenascin C (TN-C) 0.48 0.46 0.48 Vitronectin 0.55 0.550.55 Beta 2 Microglobulin (B2M) 0.45 0.58 0.58 Pancreatic SecretoryTrypsin Inhibitor 0.51 0.46 0.51 (TATI) Matrix Metalloproteinase 3(MMP3) 0.68 0.57 0.68 Omentin 0.53 0.56 0.56 Interleukin 18 BindingProtein 0.58 0.60 0.60 (IL 18bp) Apolipoprotein D (ApoD) 0.62 0.56 0.62Monoctye Chemotactic Protein 4 0.46 0.56 0.56 (MCP-4) Apolipoprotein E(Apo-E) 0.51 0.61 0.61 ST2 0.56 0.47 0.56 Thrombospondin 1 0.57 0.540.57 Gastric Inhibitory Polypeptide (GIP) 0.51 0.61 0.61 MatrixMetalloproteinase 7 (MMP7) 0.60 0.56 0.60 Intercellular AdhesionMolecule 1 0.50 0.54 0.54 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.54 0.54 0.54

TABLE 68 2 Biomarker Multivariate Analysis. Combination of Tenascin C(TN-C) and Analyte 2 AUC by AUC by random logistic Maximum Analyte 2forest regression AUC Vitronectin 0.59 0.54 0.59 Beta 2 Microglobulin(B2M) 0.56 0.56 0.56 Pancreatic Secretory Trypsin Inhibitor 0.57 0.520.57 (TATI) Matrix Metalloproteinase 3 (MMP3) 0.51 0.56 0.56 Omentin0.57 0.54 0.57 Interleukin 18 Binding Protein 0.53 0.57 0.57 (IL 18bp)Apolipoprotein D (ApoD) 0.65 0.54 0.65 Monoctye Chemotactic Protein 40.52 0.53 0.53 (MCP-4) Apolipoprotein E (Apo-E) 0.51 0.62 0.62 ST2 0.500.53 0.53 Thrombospondin 1 0.60 0.49 0.60 Gastric Inhibitory Polypeptide(GIP) 0.54 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.51 0.56 0.56Intercellular Adhesion Molecule 1 0.59 0.47 0.59 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.53 0.48 0.53

TABLE 69 2 Biomarker Multivariate Analysis. Combination of Vitronectinand Analyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Beta 2 Microglobulin (B2M) 0.62 0.55 0.62 PancreaticSecretory Trypsin 0.55 0.50 0.55 Inhibitor (TATI) MatrixMetalloproteinase 3 (MMP3) 0.53 0.56 0.56 Omentin 0.55 0.48 0.55Interleukin 18 Binding Protein 0.58 0.57 0.58 (IL 18 bp) ApolipoproteinD (ApoD) 0.69 0.55 0.69 Monoctye Chemotactic Protein 4 0.53 0.51 0.53(MCP-4) Apolipoprotein E (Apo-E) 0.55 0.60 0.60 ST2 0.53 0.50 0.53Thrombospondin 1 0.52 0.49 0.52 Gastric Inhibitory Polypeptide (GIP)0.54 0.58 0.58 Matrix Metalloproteinase 7 (MMP7) 0.54 0.57 0.57Intercellular Adhesion Molecule 1 0.45 0.48 0.48 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.49 0.48 0.49

TABLE 70 2 Biomarker Multivariate Analysis. Combination of Beta 2Microglobulin (B2M) and Analyte 2 AUC by AUC by random logistic MaximumAnalyte 2 forest regression AUC Pancreatic Secretory Trypsin Inhibitor0.53 0.56 0.56 (TATI) Matrix Metalloproteinase 3 (MMP3) 0.60 0.57 0.60Omentin 0.52 0.56 0.56 Interleukin 18 Binding Protein 0.52 0.58 0.58 (IL18 bp) Apolipoprotein D (ApoD) 0.66 0.57 0.66 Monoctye ChemotacticProtein 4 0.66 0.55 0.66 (MCP-4) Apolipoprotein E (Apo-E) 0.49 0.60 0.60ST2 0.53 0.56 0.56 Thrombospondin 1 0.54 0.55 0.55 Gastric InhibitoryPolypeptide (GIP) 0.51 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.510.58 0.58 Intercellular Adhesion Molecule 1 0.51 0.56 0.56 (ICAM-1)Dickkopf Related Protein 1 (DKK1) 0.56 0.56 0.56

TABLE 71 2 Biomarker Multivariate Analysis. Combination of PancreaticSecretory Trypsin Inhibitor (TATI) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC MatrixMetalloproteinase 3 (MMP3) 0.60 0.54 0.60 Omentin 0.58 0.51 0.58Interleukin 18 Binding Protein 0.55 0.57 0.57 (IL 18 bp) ApolipoproteinD (ApoD) 0.56 0.54 0.56 Monoctye Chemotactic Protein 4 0.58 0.48 0.58(MCP-4) Apolipoprotein E (Apo-E) 0.58 0.60 0.60 ST2 0.55 0.49 0.55Thrombospondin 1 0.50 0.53 0.53 Gastric Inhibitory Polypeptide (GIP)0.54 0.58 0.58 Matrix Metalloproteinase 7 (MMP7) 0.50 0.55 0.55Intercellular Adhesion Molecule 1 0.61 0.49 0.61 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.52 0.50 0.52

TABLE 72 2 Biomarker Multivariate Analysis. Combination of MatrixMetalloproteinase 3 (MMP3) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Omentin 0.57 0.55 0.57Interleukin 18 Binding Protein 0.62 0.59 0.62 (IL 18 bp) ApolipoproteinD (ApoD) 0.69 0.57 0.69 Monoctye Chemotactic Protein 4 0.52 0.55 0.55(MCP-4) Apolipoprotein E (Apo-E) 0.56 0.60 0.60 ST2 0.61 0.54 0.61Thrombospondin 1 0.67 0.54 0.67 Gastric Inhibitory Polypeptide (GIP)0.52 0.57 0.57 Matrix Metalloproteinase 7 (MMP7) 0.64 0.58 0.64Intercellular Adhesion Molecule 1 0.51 0.55 0.55 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.54 0.56 0.56

TABLE 73 2 Biomarker Multivariate Analysis. Combination of Omentin andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Interleukin 18 Binding Protein 0.54 0.58 0.58 (IL 18 bp)Apolipoprotein D (ApoD) 0.55 0.56 0.56 Monoctye Chemotactic Protein 40.69 0.52 0.69 (MCP-4) Apolipoprotein E (Apo-E) 0.54 0.60 0.60 ST2 0.560.48 0.56 Thrombospondin 1 0.51 0.50 0.51 Gastric Inhibitory Polypeptide(GIP) 0.66 0.57 0.66 Matrix Metalloproteinase 7 (MMP7) 0.51 0.56 0.56Intercellular Adhesion Molecule 1 0.60 0.48 0.60 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.66 0.48 0.66

TABLE 74 2 Biomarker Multivariate Analysis. Combination of Interleukin18 Binding Protein (IL 18 bp) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC Apolipoprotein D (ApoD)0.63 0.58 0.63 Monoctye Chemotactic Protein 4 0.53 0.58 0.58 (MCP-4)Apolipoprotein E (Apo-E) 0.52 0.61 0.61 ST2 0.57 0.58 0.58Thrombospondin 1 0.65 0.58 0.65 Gastric Inhibitory Polypeptide (GIP)0.52 0.61 0.61 Matrix Metalloproteinase 7 (MMP7) 0.55 0.60 0.60Intercellular Adhesion Molecule 1 0.51 0.57 0.57 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.53 0.59 0.59

TABLE 75 2 Biomarker Multivariate Analysis. Combination ofApolipoprotein D (ApoD) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Monoctye Chemotactic Protein 40.59 0.56 0.59 (MCP-4) Apolipoprotein E (Apo-E) 0.61 0.60 0.61 ST2 0.590.55 0.59 Thrombospondin 1 0.62 0.54 0.62 Gastric Inhibitory Polypeptide(GIP) 0.58 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.65 0.57 0.65Intercellular Adhesion Molecule 1 0.56 0.55 0.56 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.55 0.55 0.55

TABLE 76 2 Biomarker Multivariate Analysis. Combination of MonocyteChemotactic Protein 4 (MCP-4) and Analyte 2 AUC by AUC by randomlogistic Maximum Analyte 2 forest regression AUC Apolipoprotein E(Apo-E) 0.51 0.61 0.61 ST2 0.54 0.54 0.54 Thrombospondin 1 0.57 0.510.57 Gastric Inhibitory Polypeptide (GIP) 0.54 0.60 0.60 MatrixMetalloproteinase 7 (MMP7) 0.51 0.55 0.55 Intercellular AdhesionMolecule 1 0.53 0.52 0.53 (ICAM-1) Dickkopf Related Protein 1 (DKK1)0.60 0.52 0.60

TABLE 77 2 Biomarker Multivariate Analysis. Combination ofApolipoprotein E (Apo-E) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC ST2 0.56 0.60 0.60Thrombospondin 1 0.51 0.60 0.60 Gastric Inhibitory Polypeptide (GIP)0.53 0.61 0.61 Matrix Metalloproteinase 7 (MMP7) 0.48 0.61 0.61Intercellular Adhesion Molecule 1 0.58 0.61 0.61 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.52 0.61 0.61

TABLE 78 2 Biomarker Multivariate Analysis. Combination of ST2 andAnalyte 2 AUC by AUC by random logistic Maximum Analyte 2 forestregression AUC Thrombospondin 1 0.56 0.53 0.56 Gastric InhibitoryPolypeptide (GIP) 0.57 0.59 0.59 Matrix Metalloproteinase 7 (MMP7) 0.490.56 0.56 Intercellular Adhesion Molecule 1 0.54 0.50 0.54 (ICAM-1)Dickkopf Related Protein 1 (DKK1) 0.47 0.50 0.50

TABLE 79 2 Biomarker Multivariate Analysis. Combination ofThrombospondin 1 and Analyte 2 AUC by AUC by random logistic MaximumAnalyte 2 forest regression AUC Gastric Inhibitory Polypeptide (GIP)0.52 0.57 0.57 Matrix Metalloproteinase 7 (MMP7) 0.54 0.56 0.56Intercellular Adhesion Molecule 1 0.56 0.54 0.56 (ICAM-1) DickkopfRelated Protein 1 (DKK1) 0.53 0.49 0.53

TABLE 80 2 Biomarker Multivariate Analysis. Combination of GastricInhibitory Polypeptide (GIP) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Matrix Metalloproteinase 7(MMP7) 0.54 0.61 0.61 Intercellular Adhesion Molecule 1 0.67 0.58 0.67(ICAM-1) Dickkopf Related Protein 1 (DKK1) 0.51 0.57 0.57

TABLE 81 2 Biomarker Multivariate Analysis. Combination of MatrixMetalloproteinase 7 (MMP7) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Intercellular Adhesion Molecule1 0.48 0.56 0.56 (ICAM-1) Dickkopf Related Protein 1 (DKK1) 0.53 0.550.55

TABLE 82 2 Biomarker Multivariate Analysis. Combination of IntercellularAdhesion Molecule 1 (ICAM-1) and Analyte 2 AUC by AUC by random logisticMaximum Analyte 2 forest regression AUC Dickkopf Related Protein 1(DKK1) 0.67 0.54 0.67

TABLE 83 Multivariate Analysis of 10, 20, 40, 60, or 80 analytes usingvarious methods Total Number of Method Analytes Analytes Mean AUC RandomForest Set 1, 2, 3, 4, and 5 80 0.761 Random Forest Set 1, 2, 3, and 460 0.79 Random Forest Set 1, 2, and 3 40 0.845 Random Forest Set 1 and 220 0.857 Random Forest Set 1 10 0.797 Gradient Boosting Set 1, 2, 3, 4,and 5 80 0.795 Gradient Boosting Set 1, 2, 3, and 4 60 0.827 GradientBoosting Set 1, 2, and 3 40 0.873 Gradient Boosting Set 1 and 2 20 0.859Gradient Boosting Set 1 10 0.785 LASSO Set 1, 2, 3, 4, and 5 80 0.819LASSO Set 1, 2, 3, and 4 60 0.829 LASSO Set 1, 2, and 3 40 0.817 LASSOSet 1 and 2 20 0.816 LASSO Set 1 10 0.754

1. A method for assessing multiple sclerosis activity in an individual,the method comprising: obtaining a dataset comprising quantitativeexpression values for a plurality of biomarkers from a test sample fromthe individual, wherein the plurality of biomarkers comprise two or morebiomarkers as shown in one or more of set 1, set 2, set 3, set 4, andset 5, wherein set 1 comprises PON1, Myoglobin, PAI1, TIMP1, SDF1,IL6Rbeta, Cystatin B, IgE, MIP3beta, and VCAM1, wherein set 2 comprisesMDC, VEGF, Ficolin 3, IgA, Factor VII, IL6R, RAGE, FIB1C, ITAC, and GH,wherein set 3 comprises HBEGF, NrCAM, GROalpha, GDF15, SCFR, Ecad,Angiogenin, Sortilin, AAT, IgM, PARC, SP-D, BAFF, ADM, PEDF, IL1ra, TBG,Microalbumin, Leptin, and Eotaxin 2, wherein set 4 comprises IGFBP2,Resistin, Cathepsin D, E-Selectin, YKL40, IL22, IL8, CA 15-3, LeptinR,IGFBP2, MCP1, PRL, Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1, HCC-4, andC3, and wherein set 5 comprises AFP, ANG-1, IL18, Gelsolin, TN-C,Vitronectin, B2M, TATI, MMP3, Omentin, IL 18bp, ApoD, MCP-4, Apo-E, ST2,Thrombospondin 1, GIP, MMP7, ICAM-1, and DKK1; applying a predictivemodel on the obtained dataset to generate a score; and determiningmultiple sclerosis activity in the individual based on the score.
 2. Themethod of claim 1, wherein the dataset comprises quantitative expressionvalues for PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B,IgE, MIP3beta, and VCAM1, wherein the multiple sclerosis activity in theindividual is a state of quiescence or exacerbation, wherein aperformance of the predictive model is characterized by an area underthe curve (AUC) ranging from 0.60 to 0.99, and wherein determiningmultiple sclerosis activity in the individual based on the scorecomprises comparing the generated score to a distribution of scores, thedistribution of scores corresponding to individuals previously diagnosedwith multiple sclerosis that have been clinically classified as being inone of a state of quiescence or exacerbation; and classifying theindividual as being in one of the state of quiescence or exacerbationbased on the comparison.
 3. The method of claim 1, wherein the datasetcomprises quantitative expression values for ten or more biomarkers. 4.The method of claim 3, wherein at least five of the ten or morebiomarkers are selected from biomarkers in set
 1. 5. The method of claim0, wherein the dataset comprises quantitative expression values fortwenty or more biomarkers.
 6. The method of claim 5, wherein at leastten of the twenty or more biomarkers are selected from biomarkers in set1 and set
 2. 7. The method of claim 0, wherein the dataset comprisesquantitative expression values for forty or more biomarkers.
 8. Themethod of claim 7, wherein at least twenty of the forty or morebiomarkers are selected from biomarkers in set 1, set 2, and set
 3. 9.The method of claim 0, wherein the dataset comprises quantitativeexpression values for sixty or more biomarkers.
 10. The method of claim9, wherein at least thirty of the sixty or more biomarkers are selectedfrom biomarkers in set 1, set 2, set 3, and set
 4. 11. The method of anyone of claims 0-10, wherein the predictive model is trained using one ofa random forest algorithm, a gradient boosting algorithm, and a Lassoalgorithm.
 12. The method of any one of claims 0-11, wherein performanceof the predictive model is characterized by an area under the curve(AUC) ranging from 0.60 to 0.99.
 13. The method of any one of claims0-11, wherein performance of the predictive model is characterized by anarea under the curve (AUC) ranging from 0.70 to 0.99.
 14. The method ofany one of claims 0-11, wherein performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.80 to0.99.
 15. The method of any one of claims 0-14, wherein the step ofobtaining the dataset comprises carrying out a multiplex immunoassay onthe test sample from the individual.
 16. The method of any one of claims0-15, wherein obtaining the dataset from the test sample comprisesobtaining the test sample and processing the test sample toexperimentally determine the dataset.
 17. The method of any one ofclaims 0-15, wherein obtaining the dataset from the test samplecomprises receiving the dataset from a third party that has processedthe test sample to experimentally determine the dataset.
 18. The methodof any one of claims 1-17, wherein the quantitative expression valuesfor the plurality of biomarkers are adjusted based on at least one ofage and gender of the individual.
 19. The method of any one of claims1-18, wherein the individual is a human.
 20. The method of any one ofclaims 1-19, wherein the test sample from the individual is a bloodsample.
 21. The method of any one of claims 1-20, wherein determiningmultiple sclerosis activity comprises determining a state of multiplesclerosis in the individual, wherein the state is quiescence orexacerbation.
 22. The method of any one of claims 1-20, whereindetermining multiple sclerosis activity comprises diagnosing theindividual with multiple sclerosis.
 23. The method of any one of claims1-22, wherein determining multiple sclerosis activity in the individualbased on the score comprises: comparing the generated score to adistribution of scores, the distribution of scores corresponding toindividuals that have been previously classified in one of a pluralityof categories of multiple sclerosis activity.
 24. The method of claim23, wherein the previous classification of individuals in the categoryof multiple sclerosis activity is based on clinical standards.
 25. Amethod for generating a predictive model for predicting multiplesclerosis activity, the method comprising: obtaining training dataderived from a plurality of individuals, the training data comprising:for each individual from the plurality of individuals: quantitativeexpression values of a plurality of biomarkers derived from a testsample obtained from the individual, wherein the plurality of biomarkerscomprise two or more biomarkers as shown in one or more of set 1, set 2,set 3, set 4, and set 5, wherein set 1 comprises PON1, Myoglobin, PAI1,TIMP1, SDF1, IL6Rbeta, Cystatin B, IgE, MIP3beta, and VCAM1, wherein set2 comprises MDC, VEGF, Ficolin 3, IgA, Factor VII, IL6R, RAGE, FIB1C,ITAC, and GH, wherein set 3 comprises HBEGF, NrCAM, GROalpha, GDF15,SCFR, Ecad, Angiogenin, Sortilin, AAT, IgM, PARC, SP-D, BAFF, ADM, PEDF,IL1ra, TBG, Microalbumin, Leptin, and Eotaxin 2, wherein set 4 comprisesIGFBP2, Resistin, Cathepsin D, E-Selectin, YKL40, IL22, IL8, CA 15-3,LeptinR, IGFBP2, MCP1, PRL, Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1,HCC-4, and C3, and wherein set 5 comprises AFP, ANG-1, IL18, Gelsolin,TN-C, Vitronectin, B2M, TATI, MMP3, Omentin, IL 18bp, ApoD, MCP-4,Apo-E, ST2, Thrombospondin 1, GIP, MMP7, ICAM-1, and DKK1; and anindication as to the multiple sclerosis activity of the individual; andtraining the predictive model using the obtained training data, whereinthe predictive model is trained on inputs comprising the quantitativeexpression values of the plurality of biomarkers and on ground truthdata comprising the indication.
 26. The method of claim 25, wherein theplurality of biomarkers comprise ten or more biomarkers.
 27. The methodof claim 26, wherein at least five of the ten or more biomarkers areselected from biomarkers in set
 1. 28. The method of claim 25, whereinthe plurality of biomarkers comprise twenty or more biomarkers.
 29. Themethod of claim 28, wherein at least ten of the twenty or morebiomarkers are selected from biomarkers in set 1 and set
 2. 30. Themethod of claim 25, wherein the plurality of biomarkers comprise fortyor more biomarkers.
 31. The method of claim 30, wherein at least twentyof the forty or more biomarkers are selected from biomarkers in set 1,set 2, and set
 3. 32. The method of claim 25, wherein the plurality ofbiomarkers comprise sixty or more biomarkers.
 33. The method of claim32, wherein at least thirty of the sixty or more biomarkers are selectedfrom biomarkers in set 1, set 2, set 3, and set
 4. 34. The method ofclaim 25, wherein biomarkers in set 1, set 2, set 3, set 4, and set 5are ranked based on an importance of each biomarker for determiningmultiple sclerosis activity, wherein biomarkers in set 1 are rankedhigher than biomarkers in set 2, wherein biomarkers in set 2 are rankedhigher than biomarkers in set 3, wherein biomarkers in set 3 are rankedhigher than biomarkers in set 4, and wherein biomarkers in set 4 areranked higher than biomarkers in set
 5. 35. The method of any one ofclaims 25-34, wherein training the prediction model comprises trainingthe prediction model using one of a random forest algorithm, gradientboosting algorithm, and Lasso algorithm.
 36. The method of any one ofclaims 25-35, wherein each individual of the plurality of individuals isa human.
 37. The method of any one of claims 25-36, wherein the testsample obtained from the individual is a blood sample.
 38. The method ofany one of claims 25-37, wherein the predictive model determines a stateof multiple sclerosis in an individual, wherein the state is quiescenceor exacerbation.
 39. The method of any one of claims 25-37, wherein thepredictive model determines whether to diagnose the individual withmultiple sclerosis.
 40. A system for determining multiple sclerosisactivity in an individual, the system comprising: a storage memory forstoring a dataset comprising quantitative expression values for aplurality of biomarkers from a test sample from the individual, whereinthe plurality of biomarkers comprise two or more biomarkers as shown inone or more of set 1, set 2, set 3, set 4, and set 5, wherein set 1comprises PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B, IgE,MIP3beta, and VCAM1, wherein set 2 comprises MDC, VEGF, Ficolin 3, IgA,Factor VII, IL6R, RAGE, FIB1C, ITAC, and GH, wherein set 3 comprisesHBEGF, NrCAM, GROalpha, GDF15, SCFR, Ecad, Angiogenin, Sortilin, AAT,IgM, PARC, SP-D, BAFF, ADM, PEDF, IL1ra, TBG, Microalbumin, Leptin, andEotaxin 2, wherein set 4 comprises IGFBP2, Resistin, Cathepsin D,E-Selectin, YKL40, IL22, IL8, CA 15-3, LeptinR, IGFBP2, MCP1, PRL,Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1, HCC-4, and C3, and wherein set5 comprises AFP, ANG-1, IL18, Gelsolin, TN-C, Vitronectin, B2M, TATI,MMP3, Omentin, IL 18bp, ApoD, MCP-4, Apo-E, ST2, Thrombospondin 1, GIP,MMP7, ICAM-1, and DKK1; and a processor communicatively coupled to thestorage memory for determining a score by applying the stored dataset asinput to a predictive model, wherein the score is predictive of anassessment of multiple sclerosis activity in the individual.
 41. Thesystem of claim 40, wherein the dataset comprises quantitativeexpression values for PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta,Cystatin B, IgE, MIP3beta, and VCAM1, wherein the multiple sclerosisactivity in the individual is a state of quiescence or exacerbation,wherein a performance of the predictive model is characterized by anarea under the curve (AUC) ranging from 0.60 to 0.99, and wherein theassessment of multiple sclerosis activity in the individual isdetermined by comparing the determined score to a distribution ofscores, the distribution of scores corresponding to individualspreviously diagnosed with multiple sclerosis that have been clinicallyclassified as being in one of a state of quiescence or exacerbation. 42.The system of claim 41, wherein the dataset comprises quantitativeexpression values for ten or more biomarkers.
 43. The system of claim42, wherein at least five of the ten or more biomarkers are selectedfrom biomarkers in set
 1. 44. The system of claim 41, wherein thedataset comprises quantitative expression values for twenty or morebiomarkers.
 45. The system of claim 44, wherein at least ten of thetwenty or more biomarkers are selected from biomarkers in set 1 and set2.
 46. The system of claim 41, wherein the dataset comprisesquantitative expression values for forty or more biomarkers.
 47. Thesystem of claim 46, wherein at least twenty of the forty or morebiomarkers are selected from biomarkers in set 1, set 2, and set
 3. 48.The system of claim 41, wherein the dataset comprises quantitativeexpression values for sixty or more biomarkers.
 49. The system of claim48, wherein at least thirty of the sixty or more biomarkers are selectedfrom biomarkers in set 1, set 2, set 3, and set
 4. 50. The system of anyone of claims 41-49, wherein the predictive model is trained using oneof a random forest algorithm, a gradient boosting algorithm, and a Lassoalgorithm.
 51. The system of any one of claims 41-50, whereinperformance of the predictive model is characterized by an area underthe curve (AUC) ranging from 0.60 to 0.99.
 52. The system of any one ofclaims 41-50, wherein performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.70 to0.99.
 53. The system of any one of claims 41-50, wherein performance ofthe predictive model is characterized by an area under the curve (AUC)ranging from 0.80 to 0.99.
 54. The system of any one of claims 41-53,wherein the dataset is obtained from a multiplex immunoassay performedon the test sample from the individual.
 55. The system of any one ofclaims 41-54, wherein the dataset is experimentally determined byprocessing the test sample.
 56. The system of any one of claims 41-55,wherein the dataset is received from a third party that has processedthe test sample to experimentally determine the dataset.
 57. The systemof any one of claims 41-56, wherein the quantitative expression valuesfor the plurality of biomarkers are adjusted based on at least one ofage and gender of the individual.
 58. The system of any one of claims41-57, wherein the individual is a human.
 59. The system of any one ofclaims 41-58, wherein the test sample from the individual is a bloodsample.
 60. The system of any one of claims 41-59, wherein theassessment of multiple sclerosis activity indicates a state of multiplesclerosis in the individual, wherein the state is quiescence orexacerbation.
 61. The system of any one of claims 41-59, wherein theassessment of multiple sclerosis activity indicates a diagnosis ofmultiple sclerosis.
 62. The system of any of of claims 41-60, whereinthe assessment of multiple sclerosis activity in the individual isdetermined by comparing the determined score to a distribution ofscores, the distribution of scores corresponding to individuals thathave been previously classified in one of a plurality of categories ofmultiple sclerosis activity.
 63. The system of claim 62, wherein theprevious classification of individuals in the category of multiplesclerosis activity is based on clinical standards.
 64. A non-transitorycomputer-readable medium storing computer code that, when executed by aprocessor of a computer, causes the processor to: obtain a datasetcomprising quantitative expression values for a plurality of biomarkersfrom a test sample from the individual, wherein the plurality ofbiomarkers comprise two or more biomarkers as shown in one or more ofset 1, set 2, set 3, set 4, and set 5, wherein set 1 comprises PON1,Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B, IgE, MIP3beta, andVCAM1, wherein set 2 comprises MDC, VEGF, Ficolin 3, IgA, Factor VII,IL6R, RAGE, FIB1C, ITAC, and GH, wherein set 3 comprises HBEGF, NrCAM,GROalpha, GDF15, SCFR, Ecad, Angiogenin, Sortilin, AAT, IgM, PARC, SP-D,BAFF, ADM, PEDF, IL1ra, TBG, Microalbumin, Leptin, and Eotaxin 2,wherein set 4 comprises IGFBP2, Resistin, Cathepsin D, E-Selectin,YKL40, IL22, IL8, CA 15-3, LeptinR, IGFBP2, MCP1, PRL, Tetranectin,CEACAM1, 6Ckine, SAP, CFHR1, HCC-4, and C3, and wherein set 5 comprisesAFP, ANG-1, IL18, Gelsolin, TN-C, Vitronectin, B2M, TATI, MMP3, Omentin,IL 18bp, ApoD, MCP-4, Apo-E, ST2, Thrombospondin 1, GIP, MMP7, ICAM-1,and DKK1; apply a predictive model on the obtained dataset to generate ascore; and determine multiple sclerosis activity in the individual basedon the score.
 65. The non-transitory computer-readable medium of claim64, wherein the dataset comprises quantitative expression values forPON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B, IgE, MIP3beta,and VCAM1, wherein the multiple sclerosis activity in the individual isa state of quiescence or exacerbation, wherein a performance of thepredictive model is characterized by an area under the curve (AUC)ranging from 0.60 to 0.99, and wherein the the computer code that causesthe processor to determine multiple sclerosis activity in the individualbased on the score further comprises computer code that causes theprocessor to: compare the generated score to a distribution of scores,the distribution of scores corresponding to individuals previouslydiagnosed with multiple sclerosis that have been clinically classifiedas being in one of a state of quiescence or exacerbation; and classifythe individual as being in one of the state of quiescence orexacerbation based on the comparison.
 66. The non-transitorycomputer-readable medium of claim 64, wherein the dataset comprisesquantitative expression values for ten or more biomarkers.
 67. Thenon-transitory computer-readable medium of claim 66, wherein at leastfive of the ten or more biomarkers are selected from biomarkers inset
 1. 68. The non-transitory computer-readable medium of claim 64,wherein the dataset comprises quantitative expression values for twentyor more biomarkers.
 69. The non-transitory computer-readable medium ofclaim 68, wherein at least ten of the twenty or more biomarkers areselected from biomarkers in set 1 and set
 2. 70. The non-transitorycomputer-readable medium of claim 64, wherein the dataset comprisesquantitative expression values for forty or more biomarkers.
 71. Thenon-transitory computer-readable medium of claim 70, wherein at leasttwenty of the forty or more biomarkers are selected from biomarkers inset 1, set 2, and set
 3. 72. The non-transitory computer-readable mediumof claim 64, wherein the dataset comprises quantitative expressionvalues for sixty or more biomarkers.
 73. The non-transitorycomputer-readable medium of claim 72, wherein at least thirty of thesixty or more biomarkers are selected from biomarkers in set 1, set 2,set 3, and set
 4. 74. The non-transitory computer-readable medium of anyone of claims 64-73, wherein the predictive model is trained using oneof a random forest algorithm, a gradient boosting algorithm, and a Lassoalgorithm.
 75. The non-transitory computer-readable medium of any one ofclaims 64-74, wherein performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.60 to0.99.
 76. The non-transitory computer-readable medium of any one ofclaims 64-75, wherein performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.70 to0.99.
 77. The non-transitory computer-readable medium of any one ofclaims 64-76, wherein performance of the predictive model ischaracterized by an area under the curve (AUC) ranging from 0.80 to0.99.
 78. The non-transitory computer-readable medium of any one ofclaims 64-77, wherein the obtained dataset is from a multipleximmunoassay performed on the test sample from the individual.
 79. Thenon-transitory computer-readable medium of any one of claims 64-78,wherein the obtained dataset is experimentally determined by processingthe test sample.
 80. The non-transitory computer-readable medium of anyone of claims 64-79, wherein the obtained dataset is received from athird party that has processed the test sample to experimentallydetermine the dataset.
 81. The non-transitory computer-readable mediumof any one of claims 64-80, wherein the quantitative expression valuesfor the plurality of biomarkers are adjusted based on at least one ofage and gender of the individual.
 82. The non-transitorycomputer-readable medium of any one of claims 64-81, wherein theindividual is a human.
 83. The non-transitory computer-readable mediumof any one of claims 64-82, wherein the test sample from the individualis a blood sample.
 84. The non-transitory computer-readable medium ofany one of claims 64-83, wherein the assessment of multiple sclerosisactivity in the individual is an indication of a state of multiplesclerosis in the individual, wherein the state is quiescence orexacerbation.
 85. The non-transitory computer-readable medium of any oneof claims 64-83, wherein the assessment of multiple sclerosis activityin the individual is a diagnosis of multiple sclerosis in theindividual.
 86. The non-transitory computer-readable medium of any oneof claims 64-85, wherein the computer code that causes the processor todetermine multiple sclerosis activity in the individual based on thescore further comprises computer code that causes the processor tocompare the generated score to a distribution of scores, thedistribution of scores corresponding to individuals that have beenpreviously classified in one of a plurality of categories of multiplesclerosis activity.
 87. The non-transitory computer-readable medium ofclaim 86, wherein the previous classification of individuals in thecategory of multiple sclerosis activity is based on clinical standards.88. A kit for diagnosing multiple sclerosis in an individual, the kitcomprising: a set of reagents for determining, from a test sampleobtained from the individual, quantitative expression values for aplurality of biomarkers from a test sample from the individual, whereinthe plurality of biomarkers comprise two or more biomarkers as shown inone or more of set 1, set 2, set 3, set 4, and set 5, wherein set 1comprises PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B, IgE,MIP3beta, and VCAM1, wherein set 2 comprises MDC, VEGF, Ficolin 3, IgA,Factor VII, IL6R, RAGE, FIB1C, ITAC, and GH, wherein set 3 comprisesHBEGF, NrCAM, GROalpha, GDF15, SCFR, Ecad, Angiogenin, Sortilin, AAT,IgM, PARC, SP-D, BAFF, ADM, PEDF, IL1ra, TBG, Microalbumin, Leptin, andEotaxin 2, wherein set 4 comprises IGFBP2, Resistin, Cathepsin D,E-Selectin, YKL40, IL22, IL8, CA 15-3, LeptinR, IGFBP2, MCP1, PRL,Tetranectin, CEACAM1, 6Ckine, SAP, CFHR1, HCC-4, and C3, and wherein set5 comprises AFP, ANG-1, IL18, Gelsolin, TN-C, Vitronectin, B2M, TATI,MMP3, Omentin, IL 18bp, ApoD, MCP-4, Apo-E, ST2, Thrombospondin 1, GIP,MMP7, ICAM-1, and DKK1; and instructions for using the set of reagentsto determine the quantitative expression values of the test sample,wherein the instructions further comprise instructions for determining ascore from the quantitative expression values using a predictive model,wherein the score is predictive of an assessment of multiple sclerosisactivity in the individual.
 89. The kit of claim 88, wherein the set ofreagents comprises reagents for determining quantitative expressionvalues for PON1, Myoglobin, PAI1, TIMP1, SDF1, IL6Rbeta, Cystatin B,IgE, MIP3beta, and VCAM1, wherein the multiple sclerosis activity in theindividual is a state of quiescence or exacerbation, wherein aperformance of the predictive model is characterized by an area underthe curve (AUC) ranging from 0.60 to 0.99, and wherein the assessment ofmultiple sclerosis activity in the individual is determined by comparingthe determined score to a distribution of scores, the distribution ofscores corresponding to individuals previously diagnosed with multiplesclerosis that have been clinically classified as being in one of astate of quiescence or exacerbation.
 90. The kit of claim 88, whereinthe set of reagents comprises reagents for determining quantitativeexpression values for ten or more biomarkers.
 91. The kit of claim 90,wherein at least five of the ten or more biomarkers are selected frombiomarkers in set
 1. 92. The kit of claim 88, wherein the set ofreagents comprises reagents for determining quantitative expressionvalues for twenty or more biomarkers.
 93. The kit of claim 92, whereinat least ten of the twenty or more biomarkers are selected frombiomarkers in set 1 and set
 2. 94. The kit of claim 88, wherein the setof reagents comprises reagents for determining quantitative expressionvalues for forty or more biomarkers.
 95. The kit of claim 84, wherein atleast twenty of the forty or more biomarkers are selected frombiomarkers in set 1, set 2, and set
 3. 96. The kit of claim 88, whereinthe set of reagents comprises reagents for determining quantitativeexpression values for sixty or more biomarkers.
 97. The kit of claim 96,wherein at least thirty of the sixty or more biomarkers are selectedfrom biomarkers in set 1, set 2, set 3, and set
 4. 98. The kit of anyone of claims 88-97, wherein the instructions further compriseinstructions for applying a predictive model to generate the assessmentof multiple sclerosis activity, the predictive model trained using oneof a random forest algorithm, a gradient boosting algorithm, and a Lassoalgorithm.
 99. The kit of claim 88-98, wherein performance of thepredictive model is characterized by an area under the curve (AUC)ranging from 0.60 to 0.99.
 100. The kit of claim 88-98, whereinperformance of the predictive model is characterized by an area underthe curve (AUC) ranging from 0.70 to 0.99.
 101. The kit of claim 88-98,wherein performance of the predictive model is characterized by an areaunder the curve (AUC) ranging from 0.80 to 0.99.
 102. The kit of any oneof claims 88-101, wherein the set of reagents are for performing amultiplex immunoassay on the test sample from the individual and whereinthe quantitative expression values of the plurality of biomarkers areobtained from the performed multiplex immunoassay.
 103. The kit of anyone of claims 88-102, wherein the instructions further compriseinstructions for adjusting the quantitative expression values for theplurality of biomarkers based on at least one of age and gender of theindividual.
 104. The kit of any one of claims 88-103, wherein theindividual is a human.
 105. The kit of any one of claims 88-104, whereinthe test sample from the individual is a blood sample.
 106. The kit ofany one of claims 88-105, wherein the assessment of multiple sclerosisactivity is an indication of a state of multiple sclerosis in theindividual, wherein the state is quiescence or exacerbation.
 107. Thekit of any one of claims 88-106, wherein the assessment of multiplesclerosis activity is a diagnosis of multiple sclerosis in theindividual.
 108. The kit of any one of claims 88-107, wherein theassessment of multiple sclerosis activity in the individual isdetermined by comparing the determined score to a distribution ofscores, the distribution of scores corresponding to individuals thathave been previously classified in one of a plurality of categories ofmultiple sclerosis activity.
 109. The kit of claim 108, wherein theprevious classification of individuals in the category of multiplesclerosis activity is based on clinical standards.